diff --git a/.env.example b/.env.example index a790e32..3a206c5 100644 --- a/.env.example +++ b/.env.example @@ -3,4 +3,4 @@ # .env is loaded by train.py automatically # hydra allows you to reference variables in .yaml configs with special syntax: ${oc.env:MY_VAR} -MY_VAR="/home/user/my/system/path" +PLINDER_MOUNT="$(pwd)/data/PLINDER" diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 42ca94a..3de51e1 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -18,18 +18,18 @@ repos: - id: check-toml - id: check-case-conflict - id: check-added-large-files - args: ["--maxkb=10000"] + args: ["--maxkb=20000"] # python code formatting - repo: https://github.com/psf/black - rev: 24.10.0 + rev: 25.1.0 hooks: - id: black args: [--line-length, "99"] # python import sorting - repo: https://github.com/PyCQA/isort - rev: 5.13.2 + rev: 6.0.1 hooks: - id: isort args: ["--profile", "black", "--filter-files"] @@ -43,7 +43,7 @@ repos: # python docstring formatting - repo: https://github.com/myint/docformatter - rev: v1.7.5 + rev: eb1df347edd128b30cd3368dddc3aa65edcfac38 # Don't autoupdate until https://github.com/PyCQA/docformatter/issues/293 is fixed hooks: - id: docformatter args: @@ -73,7 +73,7 @@ repos: # python check (PEP8), programming errors and code complexity - repo: https://github.com/PyCQA/flake8 - rev: 7.1.1 + rev: 7.1.2 hooks: - id: flake8 args: @@ -87,7 +87,7 @@ repos: # python security linter - repo: https://github.com/PyCQA/bandit - rev: "1.8.2" + rev: "1.8.3" hooks: - id: bandit args: ["-s", "B101"] @@ -108,7 +108,7 @@ repos: # md formatting - repo: https://github.com/executablebooks/mdformat - rev: 0.7.21 + rev: 0.7.22 hooks: - id: mdformat args: ["--number"] @@ -121,7 +121,7 @@ repos: # word spelling linter - repo: https://github.com/codespell-project/codespell - rev: v2.3.0 + rev: v2.4.1 hooks: - id: codespell args: diff --git a/Dockerfile b/Dockerfile index d96d319..55c0c30 100644 --- a/Dockerfile +++ b/Dockerfile @@ -20,13 +20,16 @@ RUN mkdir -p /software/flowdock WORKDIR /software/flowdock ## Clone project -RUN git clone https://github.com/BioinfoMachineLearning/FlowDock /software/flowdock +RUN git clone https://github.com/BioinfoMachineLearning/FlowDock /software/flowdock ## Create conda environment # RUN conda env create -f environments/flowdock_environment.yaml COPY environments/flowdock_environment_docker.yaml /software/flowdock/environments/flowdock_environment_docker.yaml RUN conda env create -f environments/flowdock_environment_docker.yaml +# Install ProDy without NumPy dependency +RUN python -m pip install --no-cache-dir --no-dependencies prody==2.4.1 + ## Automatically activate conda environment RUN echo "source activate flowdock" >> /etc/profile.d/conda.sh && \ echo "source /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \ @@ -34,4 +37,4 @@ RUN echo "source activate flowdock" >> /etc/profile.d/conda.sh && \ ## Default shell and command SHELL ["/bin/bash", "-l", "-c"] -CMD ["/bin/bash"] \ No newline at end of file +CMD ["/bin/bash"] diff --git a/README.md b/README.md index 887db19..3bd45fd 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ -[![Data DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14660031.svg)](https://doi.org/10.5281/zenodo.14660031) +[![Data DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15066450.svg)](https://doi.org/10.5281/zenodo.15066450) @@ -24,7 +24,7 @@ This is the official codebase of the paper **FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction** -\[[arXiv](https://arxiv.org/abs/2412.10966)\] +\[[arXiv](https://arxiv.org/abs/2412.10966)\] \[[Neurosnap](https://neurosnap.ai/service/FlowDock)\] \[[Tamarind Bio](https://app.tamarind.bio/tools/flowdock)\]
@@ -76,6 +76,7 @@ cd FlowDock mamba env create -f environments/flowdock_environment.yaml conda activate FlowDock # NOTE: one still needs to use `conda` to (de)activate environments pip3 install -e . # install local project as package +pip3 install prody==2.4.1 --no-dependencies # install ProDy without NumPy dependency ``` Download checkpoints @@ -91,7 +92,7 @@ cd ../ ```bash # pretrained FlowDock weights -wget https://zenodo.org/records/14660031/files/flowdock_checkpoints.tar.gz +wget https://zenodo.org/records/15066450/files/flowdock_checkpoints.tar.gz tar -xzf flowdock_checkpoints.tar.gz rm flowdock_checkpoints.tar.gz ``` @@ -105,19 +106,19 @@ tar -xzf flowdock_data_cache.tar.gz rm flowdock_data_cache.tar.gz # cached data for PDBBind, Binding MOAD, DockGen, and the PDB-based van der Mers (vdM) dataset -wget https://zenodo.org/records/14660031/files/flowdock_pdbbind_data.tar.gz +wget https://zenodo.org/records/15066450/files/flowdock_pdbbind_data.tar.gz tar -xzf flowdock_pdbbind_data.tar.gz rm flowdock_pdbbind_data.tar.gz -wget https://zenodo.org/records/14660031/files/flowdock_moad_data.tar.gz +wget https://zenodo.org/records/15066450/files/flowdock_moad_data.tar.gz tar -xzf flowdock_moad_data.tar.gz rm flowdock_moad_data.tar.gz -wget https://zenodo.org/records/14660031/files/flowdock_dockgen_data.tar.gz +wget https://zenodo.org/records/15066450/files/flowdock_dockgen_data.tar.gz tar -xzf flowdock_dockgen_data.tar.gz rm flowdock_dockgen_data.tar.gz -wget https://zenodo.org/records/14660031/files/flowdock_pdbsidechain_data.tar.gz +wget https://zenodo.org/records/15066450/files/flowdock_pdbsidechain_data.tar.gz tar -xzf flowdock_pdbsidechain_data.tar.gz rm flowdock_pdbsidechain_data.tar.gz ``` @@ -129,7 +130,7 @@ rm flowdock_pdbsidechain_data.tar.gz
**NOTE:** The following steps (besides downloading PDBBind and Binding MOAD's PDB files) are only necessary if one wants to fully process each of the following datasets manually. -Otherwise, preprocessed versions of each dataset can be found on [Zenodo](https://zenodo.org/records/14660031). +Otherwise, preprocessed versions of each dataset can be found on [Zenodo](https://zenodo.org/records/15066450). Download data @@ -159,6 +160,16 @@ mv pdb_2021aug02/ pdbsidechain/ cd ../ ``` +Lastly, to finetune `FlowDock` using the `PLINDER` dataset, one must first prepare this data for training + +```bash +# fetch PLINDER data (NOTE: requires ~1 hour to download and ~750G of storage) +export PLINDER_MOUNT="$(pwd)/data/PLINDER" +mkdir -p "$PLINDER_MOUNT" # create the directory if it doesn't exist + +plinder_download -y +``` + ### Generating ESM2 embeddings for each protein (optional, cached input data available on SharePoint) To generate the ESM2 embeddings for the protein inputs, @@ -260,10 +271,10 @@ python flowdock/train.py experiment=flowdock_fm python flowdock/train.py experiment=flowdock_fm trainer.max_epochs=20 data.batch_size=8 ``` -For example, override parameters to finetune `FlowDock`'s pretrained weights using a new dataset +For example, override parameters to finetune `FlowDock`'s pretrained weights using a new dataset such as [PLINDER](https://www.plinder.sh/) ```bash -python flowdock/train.py experiment=flowdock_fm data=my_new_datamodule ckpt_path=checkpoints/esmfold_prior_paper_weights.ckpt +python flowdock/train.py experiment=flowdock_fm data=plinder ckpt_path=checkpoints/esmfold_prior_paper_weights.ckpt ```
@@ -277,7 +288,7 @@ To reproduce `FlowDock`'s evaluation results for structure prediction, please re To reproduce `FlowDock`'s evaluation results for binding affinity prediction using the PDBBind dataset ```bash -python flowdock/eval.py data.test_datasets=[pdbbind] ckpt_path=checkpoints/esmfold_prior_paper_weights_EMA.ckpt trainer=gpu +python flowdock/eval.py data.test_datasets=[pdbbind] ckpt_path=checkpoints/esmfold_prior_paper_weights-EMA.ckpt trainer=gpu ... # re-run two more times to gather triplicate results ``` @@ -291,47 +302,55 @@ Download baseline method predictions and results ```bash # cached predictions and evaluation metrics for reproducing structure prediction paper results -wget https://zenodo.org/records/14660031/files/alphafold3_baseline_method_predictions.tar.gz +wget https://zenodo.org/records/15066450/files/alphafold3_baseline_method_predictions.tar.gz tar -xzf alphafold3_baseline_method_predictions.tar.gz rm alphafold3_baseline_method_predictions.tar.gz -wget https://zenodo.org/records/14660031/files/chai_baseline_method_predictions.tar.gz +wget https://zenodo.org/records/15066450/files/chai_baseline_method_predictions.tar.gz tar -xzf chai_baseline_method_predictions.tar.gz rm chai_baseline_method_predictions.tar.gz -wget https://zenodo.org/records/14660031/files/diffdock_baseline_method_predictions.tar.gz +wget https://zenodo.org/records/15066450/files/diffdock_baseline_method_predictions.tar.gz tar -xzf diffdock_baseline_method_predictions.tar.gz rm diffdock_baseline_method_predictions.tar.gz -wget https://zenodo.org/records/14660031/files/dynamicbind_baseline_method_predictions.tar.gz +wget https://zenodo.org/records/15066450/files/dynamicbind_baseline_method_predictions.tar.gz tar -xzf dynamicbind_baseline_method_predictions.tar.gz rm dynamicbind_baseline_method_predictions.tar.gz -wget https://zenodo.org/records/14660031/files/flowdock_baseline_method_predictions.tar.gz +wget https://zenodo.org/records/15066450/files/flowdock_baseline_method_predictions.tar.gz tar -xzf flowdock_baseline_method_predictions.tar.gz rm flowdock_baseline_method_predictions.tar.gz -wget https://zenodo.org/records/14660031/files/flowdock_aft_baseline_method_predictions.tar.gz +wget https://zenodo.org/records/15066450/files/flowdock_aft_baseline_method_predictions.tar.gz tar -xzf flowdock_aft_baseline_method_predictions.tar.gz rm flowdock_aft_baseline_method_predictions.tar.gz -wget https://zenodo.org/records/14660031/files/flowdock_esmfold_baseline_method_predictions.tar.gz +wget https://zenodo.org/records/15066450/files/flowdock_pft_baseline_method_predictions.tar.gz +tar -xzf flowdock_pft_baseline_method_predictions.tar.gz +rm flowdock_pft_baseline_method_predictions.tar.gz + +wget https://zenodo.org/records/15066450/files/flowdock_esmfold_baseline_method_predictions.tar.gz tar -xzf flowdock_esmfold_baseline_method_predictions.tar.gz rm flowdock_esmfold_baseline_method_predictions.tar.gz -wget https://zenodo.org/records/14660031/files/flowdock_hp_baseline_method_predictions.tar.gz +wget https://zenodo.org/records/15066450/files/flowdock_chai_baseline_method_predictions.tar.gz +tar -xzf flowdock_chai_baseline_method_predictions.tar.gz +rm flowdock_chai_baseline_method_predictions.tar.gz + +wget https://zenodo.org/records/15066450/files/flowdock_hp_baseline_method_predictions.tar.gz tar -xzf flowdock_hp_baseline_method_predictions.tar.gz rm flowdock_hp_baseline_method_predictions.tar.gz -wget https://zenodo.org/records/14660031/files/neuralplexer_baseline_method_predictions.tar.gz +wget https://zenodo.org/records/15066450/files/neuralplexer_baseline_method_predictions.tar.gz tar -xzf neuralplexer_baseline_method_predictions.tar.gz rm neuralplexer_baseline_method_predictions.tar.gz -wget https://zenodo.org/records/14660031/files/vina_p2rank_baseline_method_predictions.tar.gz +wget https://zenodo.org/records/15066450/files/vina_p2rank_baseline_method_predictions.tar.gz tar -xzf vina_p2rank_baseline_method_predictions.tar.gz rm vina_p2rank_baseline_method_predictions.tar.gz -wget https://zenodo.org/records/14660031/files/rfaa_baseline_method_predictions.tar.gz +wget https://zenodo.org/records/15066450/files/rfaa_baseline_method_predictions.tar.gz tar -xzf rfaa_baseline_method_predictions.tar.gz rm rfaa_baseline_method_predictions.tar.gz ``` @@ -353,13 +372,13 @@ jupyter notebook notebooks/casp16_binding_affinity_prediction_results_plotting.i For example, generate new protein-ligand complexes for a pair of protein sequence and ligand SMILES strings such as those of the PDBBind 2020 test target `6i67` ```bash -python flowdock/sample.py ckpt_path=checkpoints/esmfold_prior_paper_weights_EMA.ckpt model.cfg.prior_type=esmfold sampling_task=batched_structure_sampling input_receptor='YNKIVHLLVAEPEKIYAMPDPTVPDSDIKALTTLCDLADRELVVIIGWAKHIPGFSTLSLADQMSLLQSAWMEILILGVVYRSLFEDELVYADDYIMDEDQSKLAGLLDLNNAILQLVKKYKSMKLEKEEFVTLKAIALANSDSMHIEDVEAVQKLQDVLHEALQDYEAGQHMEDPRRAGKMLMTLPLLRQTSTKAVQHFYNKLEGKVPMHKLFLEMLEAKV' input_ligand='"c1cc2c(cc1O)CCCC2"' input_template=data/pdbbind/pdbbind_holo_aligned_esmfold_structures/6i67_holo_aligned_esmfold_protein.pdb sample_id='6i67' out_path='./6i67_sampled_structures/' n_samples=5 chunk_size=5 num_steps=40 sampler=VDODE sampler_eta=1.0 start_time='1.0' use_template=true separate_pdb=true visualize_sample_trajectories=true auxiliary_estimation_only=false esmfold_chunk_size=null trainer=gpu +python flowdock/sample.py ckpt_path=checkpoints/esmfold_prior_paper_weights-EMA.ckpt model.cfg.prior_type=esmfold sampling_task=batched_structure_sampling input_receptor='YNKIVHLLVAEPEKIYAMPDPTVPDSDIKALTTLCDLADRELVVIIGWAKHIPGFSTLSLADQMSLLQSAWMEILILGVVYRSLFEDELVYADDYIMDEDQSKLAGLLDLNNAILQLVKKYKSMKLEKEEFVTLKAIALANSDSMHIEDVEAVQKLQDVLHEALQDYEAGQHMEDPRRAGKMLMTLPLLRQTSTKAVQHFYNKLEGKVPMHKLFLEMLEAKV' input_ligand='"c1cc2c(cc1O)CCCC2"' input_template=data/pdbbind/pdbbind_holo_aligned_esmfold_structures/6i67_holo_aligned_esmfold_protein.pdb sample_id='6i67' out_path='./6i67_sampled_structures/' n_samples=5 chunk_size=5 num_steps=40 sampler=VDODE sampler_eta=1.0 start_time='1.0' use_template=true separate_pdb=true visualize_sample_trajectories=true auxiliary_estimation_only=false esmfold_chunk_size=null trainer=gpu ``` Or, for example, generate new protein-ligand complexes for pairs of protein sequences and (multi-)ligand SMILES strings (delimited via `|`) such as those of the CASP15 target `T1152` ```bash -python flowdock/sample.py ckpt_path=checkpoints/esmfold_prior_paper_weights_EMA.ckpt model.cfg.prior_type=esmfold sampling_task=batched_structure_sampling input_receptor='MYTVKPGDTMWKIAVKYQIGISEIIAANPQIKNPNLIYPGQKINIP|MYTVKPGDTMWKIAVKYQIGISEIIAANPQIKNPNLIYPGQKINIP|MYTVKPGDTMWKIAVKYQIGISEIIAANPQIKNPNLIYPGQKINIPN' input_ligand='"CC(=O)NC1C(O)OC(CO)C(OC2OC(CO)C(OC3OC(CO)C(O)C(O)C3NC(C)=O)C(O)C2NC(C)=O)C1O"' input_template=data/test_cases/predicted_structures/T1152.pdb sample_id='T1152' out_path='./T1152_sampled_structures/' n_samples=5 chunk_size=5 num_steps=40 sampler=VDODE sampler_eta=1.0 start_time='1.0' use_template=true separate_pdb=true visualize_sample_trajectories=true auxiliary_estimation_only=false esmfold_chunk_size=null trainer=gpu +python flowdock/sample.py ckpt_path=checkpoints/esmfold_prior_paper_weights-EMA.ckpt model.cfg.prior_type=esmfold sampling_task=batched_structure_sampling input_receptor='MYTVKPGDTMWKIAVKYQIGISEIIAANPQIKNPNLIYPGQKINIP|MYTVKPGDTMWKIAVKYQIGISEIIAANPQIKNPNLIYPGQKINIP|MYTVKPGDTMWKIAVKYQIGISEIIAANPQIKNPNLIYPGQKINIPN' input_ligand='"CC(=O)NC1C(O)OC(CO)C(OC2OC(CO)C(OC3OC(CO)C(O)C(O)C3NC(C)=O)C(O)C2NC(C)=O)C1O"' input_template=data/test_cases/predicted_structures/T1152.pdb sample_id='T1152' out_path='./T1152_sampled_structures/' n_samples=5 chunk_size=5 num_steps=40 sampler=VDODE sampler_eta=1.0 start_time='1.0' use_template=true separate_pdb=true visualize_sample_trajectories=true auxiliary_estimation_only=false esmfold_chunk_size=null trainer=gpu ``` If you do not already have a template protein structure available for your target of interest, set `input_template=null` to instead have the sampling script predict the ESMFold structure of your provided `input_protein` sequence before running the sampling pipeline. For more information regarding the input arguments available for sampling, please refer to the config at `configs/sample.yaml`. @@ -369,7 +388,7 @@ If you do not already have a template protein structure available for your targe For instance, one can perform batched prediction as follows: ```bash -python flowdock/sample.py ckpt_path=checkpoints/esmfold_prior_paper_weights_EMA.ckpt model.cfg.prior_type=esmfold sampling_task=batched_structure_sampling csv_path='./data/test_cases/prediction_inputs/flowdock_batched_inputs.csv' out_path='./T1152_batch_sampled_structures/' n_samples=5 chunk_size=5 num_steps=40 sampler=VDODE sampler_eta=1.0 start_time='1.0' use_template=true separate_pdb=true visualize_sample_trajectories=false auxiliary_estimation_only=false esmfold_chunk_size=null trainer=gpu +python flowdock/sample.py ckpt_path=checkpoints/esmfold_prior_paper_weights-EMA.ckpt model.cfg.prior_type=esmfold sampling_task=batched_structure_sampling csv_path='./data/test_cases/prediction_inputs/flowdock_batched_inputs.csv' out_path='./T1152_batch_sampled_structures/' n_samples=5 chunk_size=5 num_steps=40 sampler=VDODE sampler_eta=1.0 start_time='1.0' use_template=true separate_pdb=true visualize_sample_trajectories=false auxiliary_estimation_only=false esmfold_chunk_size=null trainer=gpu ``` diff --git a/configs/data/plinder.yaml b/configs/data/plinder.yaml new file mode 100644 index 0000000..658a89b --- /dev/null +++ b/configs/data/plinder.yaml @@ -0,0 +1,18 @@ +_target_: flowdock.data.plinder_datamodule.PlinderDataModule +data_dir: ${paths.data_dir}/PLINDER/ +batch_size: 16 # Needs to be divisible by the number of devices (e.g., if in a distributed setup) +num_workers: 4 +pin_memory: True +# overfitting arguments +overfitting_example_name: null # NOTE: currently not used +# model arguments +n_protein_patches: 96 +n_lig_patches: 32 +epoch_frac: 1.0 +edge_crop_size: 400000 +esm_version: ${model.cfg.protein_encoder.esm_version} +esm_repr_layer: ${model.cfg.protein_encoder.esm_repr_layer} +# general dataset arguments +plinder_offline: False +min_protein_length: 50 +max_protein_length: 750 diff --git a/environments/flowdock_environment.yaml b/environments/flowdock_environment.yaml index a74a7f8..ab55337 100644 --- a/environments/flowdock_environment.yaml +++ b/environments/flowdock_environment.yaml @@ -1,448 +1,540 @@ name: FlowDock channels: - - pyg - - iopath - pytorch + - pyg - senyan.dev - nvidia - - defaults - conda-forge dependencies: - _libgcc_mutex=0.1=conda_forge - - _openmp_mutex=4.5=2_kmp_llvm - - aiohttp=3.9.3=py39hd1e30aa_1 - - aiosignal=1.3.1=pyhd8ed1ab_0 - - antlr-python-runtime=4.9.3=pyhd8ed1ab_1 - - anyio=4.2.0=py39h06a4308_0 - - argon2-cffi=23.1.0=pyhd8ed1ab_0 - - argon2-cffi-bindings=21.2.0=py39hd1e30aa_4 - - arpack=3.7.0=hdefa2d7_2 - - arrow=1.2.3=py39h06a4308_1 - - astunparse=1.6.3=pyhd8ed1ab_0 - - async-lru=2.0.4=pyhd8ed1ab_0 - - async-timeout=4.0.3=pyhd8ed1ab_0 - - attrs=22.1.0=py39h06a4308_0 - - babel=2.14.0=pyhd8ed1ab_0 - - backcall=0.2.0=pyhd3eb1b0_0 - - backoff=2.2.1=pyhd8ed1ab_0 - - beautifulsoup4=4.12.2=py39h06a4308_0 + - _openmp_mutex=4.5=3_kmp_llvm + - aiohappyeyeballs=2.6.1=pyhd8ed1ab_0 + - aiohttp=3.11.13=py310h89163eb_0 + - aiosignal=1.3.2=pyhd8ed1ab_0 + - alsa-lib=1.2.13=hb9d3cd8_0 + - ambertools=24.8=cuda_None_nompi_py310h834fefc_101 + - annotated-types=0.7.0=pyhd8ed1ab_1 + - anyio=4.8.0=pyhd8ed1ab_0 + - aom=3.9.1=hac33072_0 + - argon2-cffi=23.1.0=pyhd8ed1ab_1 + - argon2-cffi-bindings=21.2.0=py310ha75aee5_5 + - arpack=3.9.1=nompi_hf03ea27_102 + - arrow=1.3.0=pyhd8ed1ab_1 + - asttokens=3.0.0=pyhd8ed1ab_1 + - async-lru=2.0.4=pyhd8ed1ab_1 + - async-timeout=5.0.1=pyhd8ed1ab_1 + - attr=2.5.1=h166bdaf_1 + - attrs=25.3.0=pyh71513ae_0 + - babel=2.17.0=pyhd8ed1ab_0 + - beautifulsoup4=4.13.3=pyha770c72_0 - blas=2.116=mkl - blas-devel=3.9.0=16_linux64_mkl - - bleach=6.1.0=pyhd8ed1ab_0 - - blessed=1.19.1=pyhe4f9e05_2 - - blosc=1.21.5=h0f2a231_0 - - boost=1.84.0=h8003fee_2 - - boost-cpp=1.84.0=h44aadfe_2 - - bottleneck=1.3.7=py39ha9d4c09_0 - - brotli=1.0.9=h166bdaf_9 - - brotli-bin=1.0.9=h166bdaf_9 - - brotli-python=1.0.9=py39h5a03fae_9 - - bson=0.5.9=py_0 - - bzip2=1.0.8=h7f98852_4 - - c-ares=1.25.0=hd590300_0 - - c-blosc2=2.12.0=hb4ffafa_0 - - ca-certificates=2024.2.2=hbcca054_0 - - cachecontrol=0.13.1=pyhd8ed1ab_0 - - cachecontrol-with-filecache=0.13.1=pyhd8ed1ab_0 + - bleach=6.2.0=pyh29332c3_4 + - bleach-with-css=6.2.0=h82add2a_4 + - blosc=1.21.6=he440d0b_1 + - brotli=1.1.0=hb9d3cd8_2 + - brotli-bin=1.1.0=hb9d3cd8_2 + - brotli-python=1.1.0=py310hf71b8c6_2 + - bson=0.5.10=pyhd8ed1ab_0 + - bzip2=1.0.8=h4bc722e_7 + - c-ares=1.34.4=hb9d3cd8_0 + - c-blosc2=2.15.2=h3122c55_1 + - ca-certificates=2025.1.31=hbcca054_0 - cached-property=1.5.2=hd8ed1ab_1 - cached_property=1.5.2=pyha770c72_1 - - cachetools=5.3.2=pyhd8ed1ab_0 - - cairo=1.18.0=h3faef2a_0 - - certifi=2024.2.2=pyhd8ed1ab_0 - - cffi=1.15.1=py39h5eee18b_3 - - 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typing_extensions=4.12.2=pyha770c72_1 + - typing_utils=0.1.0=pyhd8ed1ab_1 + - tzdata=2025a=h78e105d_0 + - unicodedata2=16.0.0=py310ha75aee5_0 + - uri-template=1.3.0=pyhd8ed1ab_1 + - urllib3=2.3.0=pyhd8ed1ab_0 + - validators=0.34.0=pyhd8ed1ab_1 + - wayland=1.23.1=h3e06ad9_0 + - wayland-protocols=1.41=hd8ed1ab_0 + - wcwidth=0.2.13=pyhd8ed1ab_1 + - webcolors=24.11.1=pyhd8ed1ab_0 + - webencodings=0.5.1=pyhd8ed1ab_3 + - websocket-client=1.8.0=pyhd8ed1ab_1 + - wheel=0.45.1=pyhd8ed1ab_1 + - wrapt=1.17.2=py310ha75aee5_0 + - x264=1!164.3095=h166bdaf_2 + - x265=3.5=h924138e_3 + - xkeyboard-config=2.43=hb9d3cd8_0 + - xmltodict=0.14.2=pyhd8ed1ab_1 + - xorg-libice=1.1.2=hb9d3cd8_0 + - xorg-libsm=1.2.6=he73a12e_0 + - xorg-libx11=1.8.12=h4f16b4b_0 + - xorg-libxau=1.0.12=hb9d3cd8_0 + - xorg-libxcursor=1.2.3=hb9d3cd8_0 + - xorg-libxdmcp=1.1.5=hb9d3cd8_0 + - xorg-libxext=1.3.6=hb9d3cd8_0 + - xorg-libxfixes=6.0.1=hb9d3cd8_0 + - xorg-libxrender=0.9.12=hb9d3cd8_0 + - xorg-libxscrnsaver=1.2.4=hb9d3cd8_0 + - xorg-libxt=1.3.1=hb9d3cd8_0 + - yaml=0.2.5=h7f98852_2 + - yarl=1.18.3=py310h89163eb_1 + - zeromq=4.3.5=h3b0a872_7 + - zipp=3.21.0=pyhd8ed1ab_1 + - zlib=1.3.1=hb9d3cd8_2 + - zlib-ng=2.2.4=h7955e40_0 + - zstandard=0.23.0=py310ha75aee5_1 + - zstd=1.5.7=hb8e6e7a_1 - pip: - absl-py==2.1.0 - - appdirs==1.4.4 - - asttokens==2.4.1 - - beartype==0.17.2 - - biopandas==0.4.1 - - biopython>=1.79 - - colorlog==6.8.2 - - contextlib2==21.6.0 - - deprecated==1.2.14 + - alembic==1.15.1 + - amberutils==21.0 + - antlr4-python3-runtime==4.9.3 + - autopage==0.5.2 + - beartype==0.20.0 + - biopandas==0.5.1 + - biopython==1.79 + - biotite==1.1.0 + - biotraj==1.2.2 + - cfgv==3.4.0 + - cftime==1.6.4.post1 + - click==8.1.8 + - cliff==4.9.1 + - cloudpathlib==0.21.0 + - cmaes==0.11.1 + - cmd2==2.5.11 + - colorlog==6.9.0 + - distlib==0.3.9 - git+https://github.com/NVIDIA/dllogger.git@0540a43971f4a8a16693a9de9de73c1072020769 - - dm-tree==0.1.8 + - dm-tree==0.1.9 - docker-pycreds==0.4.0 - - einops==0.7.0 - - executing==2.0.1 + - duckdb==1.2.1 + - edgembar==3.0 + - einops==0.8.1 + - eval-type-backport==0.2.2 + - executing==2.2.0 - fair-esm==2.0.0 - fairscale==0.4.13 - - fastcore==1.5.29 - - fsspec==2024.2.0 - - gitdb==4.0.11 - - gitpython==3.1.42 + - fastcore==1.7.29 + - future==1.0.0 + - fvcore==0.1.5.post20221221 + - gcsfs==2025.3.0 + - gemmi==0.7.0 + - gitdb==4.0.12 + - gitpython==3.1.44 + - google-api-core==2.24.2 + - google-auth==2.38.0 + - google-auth-oauthlib==1.2.1 + - google-cloud-core==2.4.3 + - google-cloud-storage==3.1.0 + - google-crc32c==1.6.0 + - google-resumable-media==2.7.2 + - googleapis-common-protos==1.69.1 - hydra-colorlog==1.2.0 - - lightning==2.2.2 - - lightning-utilities==0.11.1 - - lovely-numpy==0.2.11 - - lovely-tensors==0.1.15 - - ml-collections==0.1.1 + - hydra-core==1.3.2 + - hydra-optuna-sweeper==1.2.0 + - identify==2.6.9 + - iniconfig==2.0.0 + - iopath==0.1.10 + - ipython-genutils==0.2.0 + - ipywidgets==7.8.5 + - jupyterlab-widgets==1.1.11 + - lightning==2.5.0.post0 + - lightning-utilities==0.14.1 + - looseversion==1.1.2 + - lovely-numpy==0.2.13 + - lovely-tensors==0.1.18 + - mako==1.3.9 + - markdown-it-py==3.0.0 + - mdurl==0.1.2 + - ml-collections==1.0.0 + - mmcif==0.91.0 + - mmpbsa-py==16.0 + - mmtf-python==1.1.3 + - mols2grid==2.0.0 + - msgpack==1.1.0 - msgpack-numpy==0.4.8 - - numpy==1.23.5 + - narwhals==1.30.0 + - netcdf4==1.7.2 + - nodeenv==1.9.1 + - numpy==1.26.4 + - oauthlib==3.2.2 + - omegaconf==2.3.0 - git+https://github.com/amorehead/openfold.git@fe1275099639bf7e617e09ef24d6af778647dd64 + - optuna==2.10.1 + - packmol-memgen==2025.1.29 + - pandas==2.2.3 + - pandocfilters==1.5.1 + - pbr==6.1.1 + - pdb4amber==22.0 + - plinder==0.2.24 + - plotly==6.0.0 + - pluggy==1.5.0 + - portalocker==3.1.1 - posebusters==0.2.13 - git+https://git@github.com/zrqiao/power_spherical.git@290b1630c5f84e3bb0d61711046edcf6e47200d4 - - prody==2.4.1 - - protobuf==4.25.3 + - pre-commit==4.1.0 + - prettytable==3.15.1 + # - prody==2.4.1 # NOTE: we must `pip` install Prody to skip its NumPy dependency + - propcache==0.3.0 + - proto-plus==1.26.1 + - protobuf==5.29.3 + - pyarrow==19.0.1 + - pyasn1==0.6.1 + - pyasn1-modules==0.4.1 + - pymsmt==22.0 + - pyperclip==1.9.0 + - pytest==8.3.5 - python-dotenv==1.0.1 - - pytorch-lightning==2.2.2 + - python-json-logger==3.3.0 + - pytorch-lightning==2.5.0.post0 - git+https://github.com/facebookresearch/pytorch3d.git@3da7703c5ac10039645966deddffe8db52eab8c5 + - pytraj==2.0.6 + - requests-oauthlib==2.0.0 + - rich==13.9.4 - rootutils==1.0.7 + - rsa==4.9 + - sander==22.0 - seaborn==0.13.2 - - sentry-sdk==1.40.5 - - setproctitle==1.3.3 - - smmap==5.0.1 - - torchmetrics==1.3.1 - - wandb==0.17.0 - - wrapt==1.16.0 + - sentry-sdk==2.22.0 + - setproctitle==1.3.5 + - smmap==5.0.2 + - soupsieve==2.6 + - stevedore==5.4.1 + - tabulate==0.9.0 + - termcolor==2.5.0 + - torchmetrics==1.6.3 + - virtualenv==20.29.3 + - wandb==0.19.8 + - widgetsnbextension==3.6.10 + - yacs==0.1.8 diff --git a/environments/flowdock_environment_docker.yaml b/environments/flowdock_environment_docker.yaml index 14b9d4f..10acdca 100644 --- a/environments/flowdock_environment_docker.yaml +++ b/environments/flowdock_environment_docker.yaml @@ -1,50 +1,58 @@ name: flowdock channels: - pyg - - iopath - pytorch - nvidia - defaults - conda-forge dependencies: - - hydra-core=1.3.2=pyhd8ed1ab_0 - - mendeleev=0.15.0=pyhc1e730c_0 - - msgpack-python=1.0.3=py39hd09550d_0 - - networkx=3.1=py39h06a4308_0 - - omegaconf=2.3.0=pyhd8ed1ab_0 - - pandas=2.1.4=py39h1128e8f_0 - - python=3.9.17=h0755675_0_cpython - - pytorch=2.2.1=py3.9_cuda11.8_cudnn8.7.0_0 - - pytorch-cuda=11.8=h7e8668a_5 + - mendeleev=0.20.1=pymin39_ha308f57_3 + - networkx=3.4.2=pyh267e887_2 + - python=3.10.16=he725a3c_1_cpython + - pytorch=2.2.1=py3.10_cuda11.8_cudnn8.7.0_0 + - pytorch-cuda=11.8=h7e8668a_6 - pytorch-mutex=1.0=cuda - - pytorch-scatter=2.1.2=py39_torch_2.2.0_cu118 - - rdkit=2024.03.1=py39h6cc1c65_0 - - rich=13.3.5=py39h06a4308_0 - - scikit-learn=1.4.1.post1=py39ha22ef79_0 - - scipy=1.12.0=py39h474f0d3_2 - - torchaudio=2.2.1=py39_cu118 - - torchtriton=2.2.0=py39 - - torchvision=0.17.1=py39_cu118 - - tqdm=4.66.1=pyhd8ed1ab_0 + - pytorch-scatter=2.1.2=py310_torch_2.2.0_cu118 + - rdkit=2024.09.6=py310hcd13295_0 + - scikit-learn=1.6.1=py310h27f47ee_0 + - scipy=1.15.2=py310h1d65ade_0 + - torchaudio=2.2.1=py310_cu118 + - torchtriton=2.2.0=py310 + - torchvision=0.17.1=py310_cu118 + - tqdm=4.67.1=pyhd8ed1ab_1 - pip: - - beartype==0.17.2 - - biopandas==0.4.1 - - biopython>=1.79 - - dm-tree==0.1.8 - - einops==0.7.0 + - beartype==0.20.0 + - biopandas==0.5.1 + - biopython==1.79 + - biotite==1.1.0 + - git+https://github.com/NVIDIA/dllogger.git@0540a43971f4a8a16693a9de9de73c1072020769 + - dm-tree==0.1.9 + - einops==0.8.1 - fair-esm==2.0.0 + - fairscale==0.4.13 + - gemmi==0.7.0 - hydra-colorlog==1.2.0 - - lightning==2.2.2 - - lightning-utilities==0.11.1 - - lovely-numpy==0.2.11 - - lovely-tensors==0.1.15 - - ml-collections==0.1.1 + - hydra-core==1.3.2 + - hydra-optuna-sweeper==1.2.0 + - lightning==2.5.0.post0 + - lightning-utilities==0.14.1 + - lovely-numpy==0.2.13 + - lovely-tensors==0.1.18 + - ml-collections==1.0.0 + - msgpack==1.1.0 - msgpack-numpy==0.4.8 - - numpy==1.23.5 + - numpy==1.26.4 + - omegaconf==2.3.0 - git+https://github.com/amorehead/openfold.git@fe1275099639bf7e617e09ef24d6af778647dd64 - - prody==2.4.1 - - pytorch-lightning==2.2.2 + - pandas==2.2.3 + - plinder==0.2.24 + - plotly==6.0.0 + - posebusters==0.2.13 + # - prody==2.4.1 # NOTE: we must `pip` install Prody to skip its NumPy dependency + - pytorch-lightning==2.5.0.post0 - git+https://github.com/facebookresearch/pytorch3d.git@3da7703c5ac10039645966deddffe8db52eab8c5 + - rich==13.9.4 - rootutils==1.0.7 - - torchmetrics==1.3.1 - - wandb==0.17.0 + - seaborn==0.13.2 + - torchmetrics==1.6.3 + - wandb==0.19.8 diff --git a/flowdock/data/components/plinder_dataset.py b/flowdock/data/components/plinder_dataset.py new file mode 100644 index 0000000..9ff82aa --- /dev/null +++ b/flowdock/data/components/plinder_dataset.py @@ -0,0 +1,337 @@ +import glob +import os +import random +import tempfile +from pathlib import Path + +import esm +import numpy as np +import pandas as pd +import rootutils +import torch +from beartype.typing import Any, Callable, Dict +from plinder.core.index.system import PlinderSystem +from plinder.core.structure.structure import Structure +from rdkit import Chem +from torch.utils.data import Dataset + +rootutils.setup_root(__file__, indicator=".project-root", pythonpath=True) + +from flowdock.data.components.mol_features import process_molecule +from flowdock.utils import RankedLogger +from flowdock.utils.data_utils import ( + centralize_complex_graph, + combine_molecules, + merge_protein_and_ligands, + pdb_filepath_to_protein, + process_protein, +) +from flowdock.utils.model_utils import extract_esm_embeddings, sample_inplace_to_torch + +log = RankedLogger(__name__, rank_zero_only=False) + + +def flowdock_structure_featurizer( + apo_structure: Structure, + holo_structure: Structure, + chain_mapping: Dict[str, str], + esm_model: esm.ESM2, + esm_alphabet: esm.data.Alphabet, + esm_batch_converter: esm.data.BatchConverter, + esm_repr_layer: int, + n_lig_patches: int, +) -> dict[str, Any]: + """Featurize Plinder apo and holo `Structure` objects and return a dictionary of features for + FlowDock. + + :param apo_structure: The apo `Structure` object to featurize. + :param holo_structure: The holo `Structure` object to featurize. + :param chain_mapping: A dictionary mapping chain IDs from the holo structure to the apo structure. + :param esm_model: The ESM model to use for protein sequence featurization. + :param esm_alphabet: The ESM alphabet to use for protein sequence featurization. + :param esm_batch_converter: The ESM batch converter to use for protein sequence featurization. + :param esm_repr_layer: The ESM representation layer to use for protein sequence featurization. + :param n_lig_patches: The number of ligand patches to use. + :return: A dictionary of features for FlowDock. + """ + try: + # match holo protein to apo protein + apo_structure.set_chain(holo_structure.protein_chain_ordered[0]) + holo_structure, apo_structure = holo_structure.align_common_sequence(apo_structure) + apo_structure, apo_structure_raw_rmsd, apo_structure_refined_rmsd = ( + apo_structure.superimpose(holo_structure) + ) + + with ( + tempfile.NamedTemporaryFile(delete=False, suffix=".pdb") as tmp_apo_pdb, + tempfile.NamedTemporaryFile(delete=False, suffix=".pdb") as tmp_holo_pdb, + ): + apo_protein_pdb_filepath = tmp_apo_pdb.name + holo_protein_pdb_filepath = tmp_holo_pdb.name + + map_chain_id = np.vectorize(lambda x: chain_mapping.get(x, x.split(".")[-1])) + + apo_structure.protein_atom_array.chain_id = map_chain_id( + apo_structure.protein_atom_array.chain_id + ) + holo_structure.protein_atom_array.chain_id = map_chain_id( + holo_structure.protein_atom_array.chain_id + ) + + apo_structure.save_to_disk(Path(apo_protein_pdb_filepath)) + holo_structure.save_to_disk(Path(holo_protein_pdb_filepath)) + + # process ligands + ligands = [ + Chem.MolFromMolFile(ligand_sdf) for ligand_sdf in holo_structure.ligand_sdfs.values() + ] + lig_samples = [ + process_molecule( + ligand, + ref_conf_xyz=np.array(ligand.GetConformer().GetPositions()), + return_as_dict=True, + ) + for ligand in ligands + ] + for lig_sample in lig_samples: + lig_sample["metadata"]["sample_ID"] = holo_structure.id + + # process holo protein + holo_af_protein = pdb_filepath_to_protein(holo_protein_pdb_filepath) + holo_protein_sample = process_protein( + holo_af_protein, + sample_name=f"{holo_structure.id}_", + ) + complex_graph = holo_protein_sample + + if np.isnan(complex_graph["features"]["res_atom_positions"]).any(): + raise ValueError( + f"NaN values found in holo protein sample for system {holo_structure.id}" + ) + + # process apo protein + apo_af_protein = pdb_filepath_to_protein(apo_protein_pdb_filepath) + if not np.array_equal( + np.int_(holo_af_protein.aatype), + np.int_(apo_af_protein.aatype), + ): + raise ValueError( + f"Apo and holo protein sequences do not match for system {holo_structure.id}" + ) + + # embed apo protein sequence with ESM2 + sequences = [ + "".join(np.array(list(chain_seq))[chain_mask]) + for (_, chain_seq, chain_mask) in apo_af_protein.letter_sequences + ] + esm_embeddings = extract_esm_embeddings( + esm_model, + esm_alphabet, + esm_batch_converter, + sequences, + device="cpu", + esm_repr_layer=esm_repr_layer, + ) + sequences_to_embeddings = { + f"{seq}:{i}": esm_embeddings[i].cpu().numpy() for i, seq in enumerate(sequences) + } + + apo_protein_sample = process_protein( + apo_af_protein, + sample_name=f"{holo_structure.id}_", + sequences_to_embeddings=sequences_to_embeddings, + ) + + # merge holo and apo proteins + for key in complex_graph.keys(): + for subkey, value in apo_protein_sample[key].items(): + complex_graph[key]["apo_" + subkey] = value + + complex_graph["metadata"]["sample_ID"] = holo_structure.id + + if np.isnan(complex_graph["features"]["apo_res_atom_positions"]).any(): + raise ValueError( + f"NaN values found in apo protein sample for system {holo_structure.id}" + ) + if ( + complex_graph["features"]["res_atom_positions"].shape + != complex_graph["features"]["apo_res_atom_positions"].shape + ): + raise ValueError( + f"Atom positions shape mismatch between holo and apo protein samples for system {holo_structure.id}" + ) + + # merge ligands with holo and apo proteins + complex_graph = merge_protein_and_ligands( + lig_samples, + complex_graph, + n_lig_patches=n_lig_patches, + ) + + complex_graph["metadata"]["mol"] = combine_molecules(ligands) + complex_graph = centralize_complex_graph(sample_inplace_to_torch(complex_graph)) + + except Exception as e: + raise e + + return complex_graph + + +class FlowDockPlinderDataset(Dataset): # type: ignore + """Creates a dataset for FlowDock from Plinder systems. + + Parameters + ---------- + data_dir : str + The directory where the data is stored + split : str + The split to sample from + featurizer: Callable[ + [Structure, int], dict[str, torch.Tensor] + ] = structure_featurizer, + Transformation to turn structure to input tensors + system_must_have_apo_or_pred : bool + Whether to filter systems that do not have apo or predicted structures + system_must_be_deposited_before : str | None + Filter systems that were deposited on or after a certain date + system_must_be_deposited_after : str | None + Filter systems that were deposited before a certain date + min_protein_length : int | None + Filter systems with protein sequence length less than this + max_protein_length : int | None + Filter systems with protein sequence length greater than this + **kwargs : Any + Additional keyword arguments to pass to the dataset + """ + + def __init__( + self, + data_dir: str, + split: str, + featurizer: Callable[ + [ + Structure, + Structure, + esm.ESM2, + esm.data.Alphabet, + esm.data.BatchConverter, + int, + int, + ], + torch.Tensor | dict[str, torch.Tensor], + ], + system_must_have_apo_or_pred: bool = True, + system_must_be_deposited_before: str | None = None, + system_must_be_deposited_after: str | None = None, + min_protein_length: int | None = None, + max_protein_length: int | None = None, + **kwargs: Any, + ): + df = pd.read_parquet( + glob.glob(os.path.join(data_dir, "*", "*", "*", "splits", "split.parquet"))[0] + ) + split_df = df[ + (df["split"] == split) + & (df["system_pass_validation_criteria"] == True) # noqa: E712 + & (df["system_pass_statistics_criteria"] == True) # noqa: E712 + & (df["system_has_apo_or_pred"] == system_must_have_apo_or_pred) # noqa: E712 + ] + assert ( + len(split_df) > 0 + ), f"No systems found for split '{split}' with apo or pred structures." + + self._system_ids = list(set(split_df["system_id"])) + self._num_examples = len(self._system_ids) + + self._featurizer = featurizer + + self.system_must_be_deposited_before = system_must_be_deposited_before + self.system_must_be_deposited_after = system_must_be_deposited_after + + self.min_protein_length = min_protein_length + self.max_protein_length = max_protein_length + + def __len__(self) -> int: + """Get the number of examples in the dataset.""" + return self._num_examples + + def __getitem__( + self, index: int + ) -> dict[str, int | str | pd.DataFrame | dict[str, str | pd.DataFrame]]: + """Get a single item from the dataset. + + :param index: The index of the item to retrieve. + :return: A dictionary containing the features of the complex graph. + """ + try: + if not 0 <= index < self._num_examples: + raise IndexError(index) + + s = PlinderSystem(system_id=self._system_ids[index]) + + if self.system_must_be_deposited_before is not None: + # filter out systems that were deposited on or after a certain date + if s.entry["release_date"] >= self.system_must_be_deposited_before: + log.info( + f"System {s.system_id} was deposited on or after {self.system_must_be_deposited_before}. Skipping..." + ) + return self.__getitem__(np.random.randint(0, self._num_examples)) + + if self.system_must_be_deposited_after is not None: + # filter out systems that were deposited before a certain date + if s.entry["release_date"] <= self.system_must_be_deposited_after: + log.info( + f"System {s.system_id} was deposited on or before {self.system_must_be_deposited_after}. Skipping..." + ) + return self.__getitem__(np.random.randint(0, self._num_examples)) + + if self.min_protein_length is not None: + # filter out systems with protein sequence length less than `min_protein_length` + if len(s.holo_structure.protein_sequence_from_structure) < self.min_protein_length: + log.info( + f"System {s.system_id} has holo protein length {len(s.holo_structure.protein_sequence_from_structure)} < {self.min_protein_length}. Skipping..." + ) + return self.__getitem__(np.random.randint(0, self._num_examples)) + + if self.max_protein_length is not None: + # filter out systems with protein sequence length greater than `max_protein_length` + if len(s.holo_structure.protein_sequence_from_structure) > self.max_protein_length: + log.info( + f"System {s.system_id} has holo protein length {len(s.holo_structure.protein_sequence_from_structure)} > {self.max_protein_length}. Skipping..." + ) + return self.__getitem__(np.random.randint(0, self._num_examples)) + + # prefer to load (random) crystal apo protein structures over (random) predicted apo protein structures + if any(struct.structure_type == "apo" for struct in s.alternate_structures.values()): + apo_structure = random.choice( # nosec + list( + struct + for struct in s.alternate_structures.values() + if struct.structure_type == "apo" + ) + ) + else: + assert s.alternate_structures, f"System {s.system_id} has no alternate structures." + apo_structure = random.choice( # nosec + list( + struct + for struct in s.alternate_structures.values() + if struct.structure_type == "pred" + ) + ) + + holo_structure = s.holo_structure + complex_graph = self._featurizer(apo_structure, holo_structure, s.chain_mapping) + + complex_graph["features"]["affinity"] = torch.tensor( + # NOTE: until https://github.com/plinder-org/plinder/issues/94 is fixed, + # PLINDER's binding affinity values cannot be used + [torch.nan for _ in range(complex_graph["metadata"]["num_molid"])], + dtype=torch.float32, + ) + + except Exception as e: + log.error(f"Skipping system at index {index} because of the error: {e}") + return self.__getitem__(np.random.randint(0, self._num_examples)) + + return complex_graph diff --git a/flowdock/data/plinder_datamodule.py b/flowdock/data/plinder_datamodule.py new file mode 100644 index 0000000..d9e3a2f --- /dev/null +++ b/flowdock/data/plinder_datamodule.py @@ -0,0 +1,280 @@ +import os +from functools import partial + +import esm +import numpy as np +import rootutils +import torch +from beartype.typing import Any, Dict, List, Literal, Optional +from lightning import LightningDataModule +from torch.utils.data import DataLoader, Dataset + +rootutils.setup_root(__file__, indicator=".project-root", pythonpath=True) + +from flowdock.data.components.mol_features import collate_samples +from flowdock.data.components.plinder_dataset import ( + FlowDockPlinderDataset, + flowdock_structure_featurizer, +) +from flowdock.utils import RankedLogger + +log = RankedLogger(__name__, rank_zero_only=True) + +DATA_PHASE = Literal["train", "val", "test"] + + +class PlinderDataModule(LightningDataModule): + """`LightningDataModule` for wrapping custom PlinderDatasets. + + A `LightningDataModule` implements 7 key methods: + + ```python + def prepare_data(self): + # Things to do on 1 GPU/TPU (not on every GPU/TPU in DDP). + # Download data, pre-process, split, save to disk, etc... + + def setup(self, stage): + # Things to do on every process in DDP. + # Load data, set variables, etc... + + def train_dataloader(self): + # return train dataloader + + def val_dataloader(self): + # return validation dataloader + + def test_dataloader(self): + # return test dataloader + + def predict_dataloader(self): + # return predict dataloader + + def teardown(self, stage): + # Called on every process in DDP. + # Clean up after fit or test. + ``` + + This allows you to share a full dataset without explaining how to download, + split, transform and process the data. + + Read the docs: + https://lightning.ai/docs/pytorch/latest/data/datamodule.html + """ + + def __init__( + self, + data_dir: str = "data/PLINDER/", + batch_size: int = 16, + num_workers: int = 0, + pin_memory: bool = False, + stage: Optional[str] = None, + plinder_offline: bool = False, + **kwargs: Any, + ) -> None: + """Initialize a `PlinderDataModule`. + + :param data_dir: The data directory. Defaults to `"data/"`. + :param batch_size: The batch size. Defaults to `16`. + :param num_workers: The number of workers. Defaults to `0`. + :param pin_memory: Whether to pin memory. Defaults to `False`. + :param plinder_offline: Whether to use the offline version of PLINDER. Defaults to `False`. + :param stage: The stage to setup. Either `"fit"`, `"validate"`, `"test"`, or `"predict"`. Defaults to ``None``. + """ + super().__init__() + + os.environ["PLINDER_MOUNT"] = os.path.abspath(data_dir) + os.environ["PLINDER_OFFLINE"] = str(plinder_offline) # maybe avoid redownloading dataset + + # this line allows to access init params with 'self.hparams' attribute + # also ensures init params will be stored in ckpt + self.save_hyperparameters(logger=False) + + log.info("Loading pretrained ESM model...") + esm_model, self.esm_alphabet = esm.pretrained.load_model_and_alphabet_hub( + self.hparams.esm_version + ) + self.esm_model = esm_model.eval().float() + self.esm_batch_converter = self.esm_alphabet.get_batch_converter() + self.esm_model.cpu() + + featurizer = partial( + flowdock_structure_featurizer, + esm_model=self.esm_model, + esm_alphabet=self.esm_alphabet, + esm_batch_converter=self.esm_batch_converter, + esm_repr_layer=self.hparams.esm_repr_layer, + n_lig_patches=self.hparams.n_lig_patches, + ) + + # prepare for dataset(s) to be loaded into rank-shared memory (e.g., when setting `trainer.strategy = "ddp_spawn"`) + self.data_train = FlowDockPlinderDataset( + data_dir, + split="train", + featurizer=featurizer, + system_must_be_deposited_before="2018-01-01", # filter out training systems deposited after this date + min_protein_length=self.hparams.min_protein_length, # filter out training systems with protein sequence length less than this + max_protein_length=self.hparams.max_protein_length, # filter out training systems with protein sequence length greater than this + use_alternate_structures=True, + ) + + self.data_val = FlowDockPlinderDataset( + data_dir, + split="val", + featurizer=featurizer, + system_must_be_deposited_after="2017-12-31", # filter out validation systems deposited on or before this date + system_must_be_deposited_before="2019-01-01", # filter out validation systems deposited after this date + min_protein_length=self.hparams.min_protein_length, # filter out validation systems with protein sequence length less than this + max_protein_length=self.hparams.max_protein_length, # filter out validation systems with protein sequence length greater than this + use_alternate_structures=True, + ) + + self.data_test = FlowDockPlinderDataset( + data_dir, + split="test", + featurizer=featurizer, + system_must_be_deposited_after="2018-12-31", # filter out test systems deposited on or before this date + min_protein_length=self.hparams.min_protein_length, # filter out test systems with protein sequence length less than this + max_protein_length=self.hparams.max_protein_length, # filter out test systems with protein sequence length greater than this + use_alternate_structures=True, + ) + + def prepare_data(self) -> None: + """Download data if needed. Lightning ensures that `self.prepare_data()` is called only + within a single process on CPU, so you can safely add your downloading logic within. In + case of multi-node training, the execution of this hook depends upon + `self.prepare_data_per_node()`. + + Do not use it to assign state (self.x = y). + """ + pass + + def setup(self, stage: Optional[str] = None) -> None: + """Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`. + + This method is called by Lightning before `trainer.fit()`, `trainer.validate()`, `trainer.test()`, and + `trainer.predict()`, so be careful not to execute things like random split twice! Also, it is called after + `self.prepare_data()` and there is a barrier in between which ensures that all the processes proceed to + `self.setup()` once the data is prepared and available for use. + + :param stage: The stage to setup. Either `"fit"`, `"validate"`, `"test"`, or `"predict"`. Defaults to ``None``. + """ + pass + + @staticmethod + def dynamic_batching_by_max_edge_count( + x: Dict[str, Any], max_n_edges: int, max_batch_size: int + ) -> Any: + """Dynamically batch by maximum edge count. + + :param x: The input graph data. If `None`, skip the current batch. + :param max_n_edges: The maximum number of edges. + :param max_batch_size: The maximum batch size. + :return: The batched data. + """ + if "num_u" in x["metadata"].keys(): + num_edges_upperbound = ( + x["metadata"]["num_a"] * 128 + x["metadata"]["num_i"] * 8 + 160**2 + ) + else: + num_edges_upperbound = x["metadata"]["num_a"] * 128 + 160**2 + batch_size = max(1, min(max_n_edges // num_edges_upperbound, max_batch_size)) + return collate_samples([x] * batch_size) + + def get_dataloader( + self, + phase: DATA_PHASE, + dataset: Dataset, + **kwargs: Dict[str, Any], + ) -> DataLoader[Any]: + """Create a dataloader from a dataset. + + :param phase: The phase of the dataset. Either `"train"`, `"val"`, or `"test"`. + :param dataset: The dataset. + :param kwargs: Additional keyword arguments to pass to the dataloader. + :return: The dataloader. + """ + if phase == "train": + batch_size = self.hparams.batch_size + epoch_frac = self.hparams.epoch_frac + else: + batch_size = 1 + epoch_frac = 1 + sampled_indices = np.random.choice( + len(dataset), + int(len(dataset) * epoch_frac), + replace=False, + ) + if phase == "val": + sampled_indices = np.repeat(sampled_indices, self.trainer.world_size) + subdataset = torch.utils.data.Subset(dataset, sampled_indices) + return DataLoader( + subdataset, + batch_size=None, + collate_fn=lambda x: self.dynamic_batching_by_max_edge_count( + x, self.hparams.edge_crop_size, batch_size + ), + num_workers=self.hparams.num_workers, + pin_memory=self.hparams.pin_memory, + **kwargs, + ) + + def train_dataloader(self) -> DataLoader[Any]: + """Create and return the train dataloader. + + :return: The train dataloader. + """ + return self.get_dataloader( + "train", + dataset=self.data_train, + shuffle=True, + ) + + def val_dataloader(self) -> List[DataLoader[Any]]: + """Create and return the validation dataloaders. + + :return: The validation dataloaders. + """ + return self.get_dataloader( + "val", + dataset=self.data_val, + shuffle=False, + ) + + def test_dataloader(self) -> List[DataLoader[Any]]: + """Create and return the test dataloaders. + + :return: The test dataloaders. + """ + return self.get_dataloader( + "test", + dataset=self.data_test, + shuffle=False, + ) + + def teardown(self, stage: Optional[str] = None) -> None: + """Lightning hook for cleaning up after `trainer.fit()`, `trainer.validate()`, + `trainer.test()`, and `trainer.predict()`. + + :param stage: The stage being torn down. Either `"fit"`, `"validate"`, `"test"`, or `"predict"`. + Defaults to ``None``. + """ + pass + + def state_dict(self) -> Dict[Any, Any]: + """Called when saving a checkpoint. Implement to generate and save the datamodule state. + + :return: A dictionary containing the datamodule state that you want to save. + """ + return {} + + def load_state_dict(self, state_dict: Dict[str, Any]) -> None: + """Called when loading a checkpoint. Implement to reload datamodule state given datamodule + `state_dict()`. + + :param state_dict: The datamodule state returned by `self.state_dict()`. + """ + pass + + +if __name__ == "__main__": + _ = PlinderDataModule() diff --git a/flowdock/utils/data_utils.py b/flowdock/utils/data_utils.py index 6b7aa11..7229f41 100644 --- a/flowdock/utils/data_utils.py +++ b/flowdock/utils/data_utils.py @@ -27,8 +27,6 @@ from rdkit.Chem import AllChem from rdkit.Geometry import Point3D -from flowdock.data.components.process_mols import read_molecule - rootutils.setup_root(__file__, indicator=".project-root", pythonpath=True) from flowdock.data.components import residue_constants @@ -37,6 +35,7 @@ collate_numpy_samples, process_mol_file, ) +from flowdock.data.components.process_mols import read_molecule from flowdock.data.components.residue_constants import restype_1to3 as af_restype_1to3 from flowdock.data.components.residue_constants import restypes as af_restypes from flowdock.models.components.transforms import LatentCoordinateConverter @@ -1216,7 +1215,7 @@ def convert_protein_pts_to_pdb( chain = Chain(chain_id) model.add(chain) - protein_chain_data = torch.load(processed_pt_filename) + protein_chain_data = torch.load(processed_pt_filename) # nosec for residue_id in range(len(protein_chain_data["seq"])): aa = Polypeptide.one_to_three(protein_chain_data["seq"][residue_id]) aa_atoms = AA_TO_LONG[AA_TO_NUM[aa]][:14] diff --git a/img/FlowDock.png b/img/FlowDock.png index f15d3d3..63a51f6 100644 Binary files a/img/FlowDock.png and b/img/FlowDock.png differ diff --git a/notebooks/casp16_flowdock_bust_results.csv b/notebooks/casp16_flowdock_bust_results.csv new file mode 100644 index 0000000..451670e --- /dev/null +++ b/notebooks/casp16_flowdock_bust_results.csv @@ -0,0 +1,302 @@ 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a/notebooks/casp16_flowdock_bust_results_relaxed.csv b/notebooks/casp16_flowdock_bust_results_relaxed.csv new file mode 100644 index 0000000..034a9f3 --- /dev/null +++ b/notebooks/casp16_flowdock_bust_results_relaxed.csv @@ -0,0 +1,302 @@ +mol_pred_loaded,mol_true_loaded,mol_cond_loaded,sanitization,inchi_convertible,all_atoms_connected,molecular_formula,molecular_bonds,double_bond_stereochemistry,tetrahedral_chirality,bond_lengths,bond_angles,internal_steric_clash,aromatic_ring_flatness,non-aromatic_ring_non-flatness,double_bond_flatness,internal_energy,protein-ligand_maximum_distance,minimum_distance_to_protein,minimum_distance_to_organic_cofactors,minimum_distance_to_inorganic_cofactors,minimum_distance_to_waters,volume_overlap_with_protein,volume_overlap_with_organic_cofactors,volume_overlap_with_inorganic_cofactors,volume_overlap_with_waters,rmsd_≤_2å,passes_valence_checks,passes_kekulization,inchi_crystal_valid,inchi_docked_valid,inchi_crystal,inchi_docked,inchi_overall,inchi_version,hydrogens,net_charge,protons,stereo_sp3,stereo_sp3_inverted,stereo_type,stereochemistry_preserved,number_bonds,shortest_bond_relative_length,longest_bond_relative_length,number_short_outlier_bonds,number_long_outlier_bonds,number_angles,most_extreme_relative_angle,number_outlier_angles,number_noncov_pairs,shortest_noncovalent_relative_distance,number_clashes,number_valid_bonds,number_valid_angles,number_valid_noncov_pairs,number_aromatic_rings_checked,number_aromatic_rings_pass,aromatic_ring_maximum_distance_from_plane,number_non-aromatic_rings_checked,number_non-aromatic_rings_pass,non-aromatic_ring_maximum_distance_from_plane,number_double_bonds_checked,number_double_bonds_pass,double_bond_maximum_distance_from_plane,ensemble_avg_energy,mol_pred_energy,energy_ratio,relative_deviation,z_value,relative_deviation_passes,z_value_passes,smallest_distance_protein,num_pairwise_clashes_protein,most_extreme_ligand_atom_id_protein,most_extreme_protein_atom_id_protein,most_extreme_ligand_element_protein,most_extreme_protein_element_protein,most_extreme_ligand_vdw_protein,most_extreme_protein_vdw_protein,most_extreme_sum_radii_protein,most_extreme_distance_protein,most_extreme_sum_radii_scaled_protein,most_extreme_relative_distance_protein,most_extreme_clash_protein,smallest_distance_organic_cofactors,not_too_far_away_organic_cofactors,num_pairwise_clashes_organic_cofactors,most_extreme_ligand_atom_id_organic_cofactors,most_extreme_protein_atom_id_organic_cofactors,most_extreme_ligand_element_organic_cofactors,most_extreme_protein_element_organic_cofactors,most_extreme_ligand_vdw_organic_cofactors,most_extreme_protein_vdw_organic_cofactors,most_extreme_sum_radii_organic_cofactors,most_extreme_distance_organic_cofactors,most_extreme_sum_radii_scaled_organic_cofactors,most_extreme_relative_distance_organic_cofactors,most_extreme_clash_organic_cofactors,smallest_distance_inorganic_cofactors,not_too_far_away_inorganic_cofactors,num_pairwise_clashes_inorganic_cofactors,most_extreme_ligand_atom_id_inorganic_cofactors,most_extreme_protein_atom_id_inorganic_cofactors,most_extreme_ligand_element_inorganic_cofactors,most_extreme_protein_element_inorganic_cofactors,most_extreme_ligand_vdw_inorganic_cofactors,most_extreme_protein_vdw_inorganic_cofactors,most_extreme_sum_radii_inorganic_cofactors,most_extreme_distance_inorganic_cofactors,most_extreme_sum_radii_scaled_inorganic_cofactors,most_extreme_relative_distance_inorganic_cofactors,most_extreme_clash_inorganic_cofactors,smallest_distance_waters,not_too_far_away_waters,num_pairwise_clashes_waters,most_extreme_ligand_atom_id_waters,most_extreme_protein_atom_id_waters,most_extreme_ligand_element_waters,most_extreme_protein_element_waters,most_extreme_ligand_vdw_waters,most_extreme_protein_vdw_waters,most_extreme_sum_radii_waters,most_extreme_distance_waters,most_extreme_sum_radii_scaled_waters,most_extreme_relative_distance_waters,most_extreme_clash_waters,volume_overlap_protein,volume_overlap_organic_cofactors,volume_overlap_inorganic_cofactors,volume_overlap_waters,rmsd,kabsch_rmsd,centroid_distance,mol_id,lig_idx 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100644 index 0000000..61c667f --- /dev/null +++ b/notebooks/casp16_flowdock_vs_multicom_ligand_structure_prediction_results_plotting.ipynb @@ -0,0 +1,205 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CASP16 FlowDock vs. MULTICOM_ligand Structure Prediction Results Plotting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Make plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# CASP16 Structure Prediction Results #\n", + "\n", + "# Prepare FlowDock and MULTICOM_ligand's results\n", + "flowdock_top_ranked_results_df = pd.read_csv(\"casp16_flowdock_bust_results.csv\")\n", + "flowdock_top_ranked_results_relaxed_df = pd.read_csv(\"casp16_flowdock_bust_results_relaxed.csv\")\n", + "\n", + "multicom_ligand_results_df = pd.read_csv(\"casp16_multicom_ligand_pose_results.csv\")\n", + "multicom_ligand_top_ranked_results_df = multicom_ligand_results_df[\n", + " (multicom_ligand_results_df[\"group\"] == \"LG207\")\n", + " & (multicom_ligand_results_df[\"model\"].str.contains(\"_1\"))\n", + "]\n", + "\n", + "# Find common targets between FlowDock and MULTICOM_ligand's completed predictions\n", + "common_targets = (\n", + " set(flowdock_top_ranked_results_df[\"mol_id\"])\n", + " .intersection(set(flowdock_top_ranked_results_relaxed_df[\"mol_id\"]))\n", + " .intersection(set(multicom_ligand_top_ranked_results_df[\"target\"]))\n", + ")\n", + "flowdock_top_ranked_results_df = flowdock_top_ranked_results_df[\n", + " flowdock_top_ranked_results_df[\"mol_id\"].isin(common_targets)\n", + "]\n", + "flowdock_top_ranked_results_relaxed_df = flowdock_top_ranked_results_relaxed_df[\n", + " flowdock_top_ranked_results_relaxed_df[\"mol_id\"].isin(common_targets)\n", + "]\n", + "multicom_ligand_top_ranked_results_df = multicom_ligand_top_ranked_results_df[\n", + " multicom_ligand_top_ranked_results_df[\"target\"].isin(common_targets)\n", + "]\n", + "\n", + "# Plot FlowDock vs. MULTICOM_ligand's results as a histogram comparison\n", + "n = max(len(flowdock_top_ranked_results_df), len(flowdock_top_ranked_results_relaxed_df))\n", + "\n", + "sns.set_theme(style=\"whitegrid\")\n", + "plt.figure(figsize=(8, 6))\n", + "ax = sns.histplot(\n", + " flowdock_top_ranked_results_df[\"rmsd\"],\n", + " bins=100,\n", + " kde=True,\n", + " color=\"green\",\n", + " label=\"FlowDock (no post-processing)\",\n", + ")\n", + "ax = sns.histplot(\n", + " flowdock_top_ranked_results_relaxed_df[\"rmsd\"],\n", + " bins=100,\n", + " kde=True,\n", + " color=\"orange\",\n", + " label=\"FlowDock (with relaxation)\",\n", + ")\n", + "ax = sns.histplot(\n", + " multicom_ligand_top_ranked_results_df[\"rmsd\"],\n", + " bins=100,\n", + " kde=True,\n", + " color=\"skyblue\",\n", + " label=\"MULTICOM_ligand (with relaxation)\",\n", + ")\n", + "plt.xlabel(\"RMSD\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.xlim(0, 10)\n", + "plt.legend(title=\"Method\")\n", + "plt.title(\"\")\n", + "plt.savefig(\n", + " f\"casp16_flowdock_vs_multicom_ligand_structure_prediction_results_comparison_n={n}.png\"\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Make plot (for multi-ligands only)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Find results for single-ligand targets only\n", + "common_multi_ligand_targets = common_targets.intersection(\n", + " set(\n", + " flowdock_top_ranked_results_df[\"mol_id\"]\n", + " .value_counts()[flowdock_top_ranked_results_df[\"mol_id\"].value_counts() > 1]\n", + " .index\n", + " ).intersection(\n", + " set(\n", + " flowdock_top_ranked_results_relaxed_df[\"mol_id\"]\n", + " .value_counts()[flowdock_top_ranked_results_relaxed_df[\"mol_id\"].value_counts() > 1]\n", + " .index\n", + " )\n", + " )\n", + ")\n", + "flowdock_top_ranked_multi_ligand_results_df = flowdock_top_ranked_results_df[\n", + " flowdock_top_ranked_results_df[\"mol_id\"].isin(common_multi_ligand_targets)\n", + "]\n", + "flowdock_top_ranked_results_relaxed_multi_ligand_df = flowdock_top_ranked_results_relaxed_df[\n", + " flowdock_top_ranked_results_relaxed_df[\"mol_id\"].isin(common_multi_ligand_targets)\n", + "]\n", + "multicom_ligand_top_ranked_multi_ligand_results_df = multicom_ligand_top_ranked_results_df[\n", + " multicom_ligand_top_ranked_results_df[\"target\"].isin(common_multi_ligand_targets)\n", + "]\n", + "\n", + "# Plot FlowDock vs. MULTICOM_ligand's multi-ligand results as a histogram comparison\n", + "n = max(\n", + " len(flowdock_top_ranked_multi_ligand_results_df),\n", + " len(flowdock_top_ranked_results_relaxed_multi_ligand_df),\n", + ")\n", + "\n", + "sns.set_theme(style=\"whitegrid\")\n", + "plt.figure(figsize=(8, 6))\n", + "ax = sns.histplot(\n", + " flowdock_top_ranked_multi_ligand_results_df[\"rmsd\"],\n", + " bins=100,\n", + " kde=True,\n", + " color=\"green\",\n", + " label=\"FlowDock (no post-processing)\",\n", + ")\n", + "ax = sns.histplot(\n", + " flowdock_top_ranked_results_relaxed_multi_ligand_df[\"rmsd\"],\n", + " bins=100,\n", + " kde=True,\n", + " color=\"orange\",\n", + " label=\"FlowDock (with relaxation)\",\n", + ")\n", + "ax = sns.histplot(\n", + " multicom_ligand_top_ranked_multi_ligand_results_df[\"rmsd\"],\n", + " bins=100,\n", + " kde=True,\n", + " color=\"skyblue\",\n", + " label=\"MULTICOM_ligand (with relaxation)\",\n", + ")\n", + "plt.xlabel(\"RMSD\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.xlim(0, 10)\n", + "plt.legend(title=\"Method\")\n", + "plt.title(\"\")\n", + "plt.savefig(\n", + " f\"casp16_flowdock_vs_multicom_ligand_multi_ligand_structure_prediction_results_comparison_n={n}.png\"\n", + ")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "PyMOL-MULTICOM_ligand", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/casp16_multicom_ligand_pose_results.csv b/notebooks/casp16_multicom_ligand_pose_results.csv new file mode 100644 index 0000000..8ecd6a3 --- /dev/null +++ b/notebooks/casp16_multicom_ligand_pose_results.csv @@ -0,0 +1,30222 @@ 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b/notebooks/dockgen_structure_prediction_relaxed_rmsd_violin_plot.png differ diff --git a/notebooks/dockgen_structure_prediction_results_plotting.ipynb b/notebooks/dockgen_structure_prediction_results_plotting.ipynb index e7b02fa..40a4be6 100644 --- a/notebooks/dockgen_structure_prediction_results_plotting.ipynb +++ b/notebooks/dockgen_structure_prediction_results_plotting.ipynb @@ -68,6 +68,7 @@ " \"neuralplexer\",\n", " \"flowdock_hp\",\n", " \"flowdock_aft\",\n", + " \"flowdock_pft\",\n", " \"flowdock_esmfold\",\n", " \"flowdock\",\n", "]\n", @@ -85,6 +86,7 @@ "globals()[\"neuralplexer_output_dir\"] = os.path.join(\"..\", \"forks\", \"NeuralPLexer\", \"inference\")\n", "globals()[\"flowdock_hp_output_dir\"] = os.path.join(\"..\", \"forks\", \"FlowDock\", \"hp_inference\")\n", "globals()[\"flowdock_aft_output_dir\"] = os.path.join(\"..\", \"forks\", \"FlowDock\", \"aft_inference\")\n", + "globals()[\"flowdock_pft_output_dir\"] = os.path.join(\"..\", \"forks\", \"FlowDock\", \"pft_inference\")\n", "globals()[\"flowdock_esmfold_output_dir\"] = os.path.join(\n", " \"..\", \"forks\", \"FlowDock\", \"esmfold_inference\"\n", ")\n", @@ -209,6 +211,20 @@ " )\n", " )\n", "\n", + " # FlowDock-PFT results\n", + " globals()[f\"flowdock_pft_dockgen_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n", + " globals()[\"flowdock_pft_output_dir\"],\n", + " f\"flowdock_dockgen_outputs_{repeat_index}\",\n", + " \"bust_results.csv\",\n", + " )\n", + " globals()[f\"flowdock_pft_dockgen_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n", + " os.path.join(\n", + " globals()[\"flowdock_pft_output_dir\"],\n", + " f\"flowdock_dockgen_outputs_{repeat_index}_relaxed\",\n", + " \"bust_results.csv\",\n", + " )\n", + " )\n", + "\n", " # FlowDock-ESMFold results\n", " globals()[f\"flowdock_esmfold_dockgen_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n", " globals()[\"flowdock_esmfold_output_dir\"],\n", @@ -246,6 +262,7 @@ " \"neuralplexer\": \"NeuralPLexer\",\n", " \"flowdock_hp\": \"FlowDock-HP\",\n", " \"flowdock_aft\": \"FlowDock-AFT\",\n", + " \"flowdock_pft\": \"FlowDock-PFT\",\n", " \"flowdock_esmfold\": \"FlowDock-ESMFold\",\n", " \"flowdock\": \"FlowDock-AF3\",\n", "}\n", @@ -260,6 +277,7 @@ " \"neuralplexer\": \"DL-based blind\",\n", " \"flowdock_hp\": \"DL-based blind\",\n", " \"flowdock_aft\": \"DL-based blind\",\n", + " \"flowdock_pft\": \"DL-based blind\",\n", " \"flowdock_esmfold\": \"DL-based blind\",\n", " \"flowdock\": \"DL-based blind\",\n", "}\n", @@ -497,7 +515,9 @@ " y=\"rmsd\",\n", " hue=\"post-processing\",\n", " # ignore outliers for better readability\n", - " data=combined_relaxed_data[combined_relaxed_data[\"rmsd\"] < 10],\n", + " data=combined_relaxed_data[\n", + " (combined_relaxed_data[\"rmsd\"] < 10) & ~(combined_relaxed_data[\"rmsd\"].isna())\n", + " ],\n", " split=True,\n", " inner=\"quartile\",\n", " palette=colors,\n", @@ -532,7 +552,7 @@ "colors = [\"#FB8072\", \"#BEBADA\"]\n", "\n", "bar_width = 0.5\n", - "r1 = [item - 0.25 for item in range(2, 24, 2)]\n", + "r1 = [item - 0.25 for item in range(2, 26, 2)]\n", "r2 = [x + bar_width for x in r1]\n", "\n", "(\n", @@ -555,11 +575,21 @@ " num_methods = len(dockgen_labels)\n", "\n", " num_dockgen_data_points = max(\n", - " len(dockgen_results_table[(dockgen_results_table[\"method\"] == method)])\n", + " len(\n", + " dockgen_results_table[\n", + " (dockgen_results_table[\"method\"] == method)\n", + " & ~(dockgen_results_table[\"rmsd\"].isna())\n", + " ]\n", + " )\n", " for method in dockgen_labels\n", " )\n", " num_dockgen_relaxed_data_points = max(\n", - " len(dockgen_relaxed_results_table[(dockgen_relaxed_results_table[\"method\"] == method)])\n", + " len(\n", + " dockgen_relaxed_results_table[\n", + " (dockgen_relaxed_results_table[\"method\"] == method)\n", + " & ~(dockgen_relaxed_results_table[\"rmsd\"].isna())\n", + " ]\n", + " )\n", " for method in dockgen_labels\n", " )\n", "\n", @@ -718,8 +748,8 @@ "dockgen_relaxed_rmsd_lt_2_and_pb_valid_data_std.fillna(0, inplace=True)\n", "\n", "# define font properties\n", - "plt.rcParams[\"font.size\"] = 24\n", - "plt.rcParams[\"axes.labelsize\"] = 26\n", + "plt.rcParams[\"font.size\"] = 22\n", + "plt.rcParams[\"axes.labelsize\"] = 24\n", "\n", "# create the figure and a list of axes\n", "fig, axis = plt.subplots(figsize=(34, 14))\n", @@ -770,7 +800,7 @@ "\n", "# add labels, titles, ticks, etc.\n", "axis.set_ylabel(\"Percentage of predictions\")\n", - "axis.set_xlim(1, 23 + 0.1)\n", + "axis.set_xlim(1, 25 + 0.1)\n", "axis.set_ylim(0, 125)\n", "\n", "assert len(dockgen_rmsd_lt_2_bar) == len(dockgen_rmsd_lt_2_and_pb_valid_bar), (\n", @@ -833,7 +863,7 @@ "axis.grid(axis=\"y\", color=\"#EAEFF8\")\n", "axis.set_axisbelow(True)\n", "\n", - "axis.set_xticks([2, 2 + 1e-3, 4, 6, 8, 10, 12, 13, 14, 16, 18, 20, 22])\n", + "axis.set_xticks([2, 2 + 1e-3, 4, 6, 8, 10, 12, 14, 14 + 1e-3, 16, 18, 20, 22, 24])\n", "axis.set_xticks([1 + 0.1], minor=True)\n", "axis.set_xticklabels(\n", " [\n", @@ -844,10 +874,11 @@ " \"RoseTTAFold-AA\",\n", " \"AF3-Single-Seq\",\n", " \"Chai-1-Single-Seq\",\n", - " \"DL-based blind\",\n", " \"NeuralPLexer\",\n", + " \"DL-based blind\",\n", " \"FlowDock-HP\",\n", " \"FlowDock-AFT\",\n", + " \"FlowDock-PFT\",\n", " \"FlowDock-ESMFold\",\n", " \"FlowDock-AF3\",\n", " ]\n", @@ -861,7 +892,7 @@ "axis.tick_params(axis=\"y\", which=\"major\", left=\"off\", right=\"on\", color=\"#EAEFF8\")\n", "\n", "# vertical alignment of xtick labels\n", - "vert_alignments = [0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0]\n", + "vert_alignments = [0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0]\n", "for tick, y in zip(axis.get_xticklabels(), vert_alignments):\n", " tick.set_y(y)\n", "\n", @@ -906,7 +937,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.17" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/notebooks/posebusters_benchmark_mol_to_protein_mapping_1.json b/notebooks/posebusters_benchmark_mol_to_protein_mapping_1.json new file mode 100644 index 0000000..062491f --- /dev/null +++ b/notebooks/posebusters_benchmark_mol_to_protein_mapping_1.json @@ -0,0 +1 @@ +{"7PK0_BYC": "Q2KIR7", "7ES1_UDP": "Q0DPB7", "7NUT_GLP": "Q9Y303", "7NF0_BYN": "G9NLP8", "7U0U_FK5": "Q4WLV6", "7D6O_MTE": "Unknown", "6VTA_AKN": "Q52424", "7L7C_XQ1": "P0AEG4", "7ZF0_DHR": "Q9SBL1", "7V43_C4O": "A0A088FL33", "6XBO_5MC": "Q61420", "7U3J_L6U": "Q8IVV7", "7OZC_G6S": "Q8A346", "7P1M_4IU": "O00214", "5SIS_JSM": "Q9Y233", "7MFP_Z7P": "Q5UPL1", "7JY3_VUD": "P68871", "7XI7_4RI": "P00374", "7VWF_K55": "Q03181", "7FHA_ADX": "O95340", "7TBU_S3P": "Q5AME2", "7R6J_2I7": "G0SFD1", "6YR2_T1C": "P0ACT4", "7L00_XCJ": "A0A031WIF2", "7VC5_9SF": "A0A7J6JUK2", "7Z7F_IF3": "Q9Y5A9", "7TSF_H4B": "P29476", "7SZA_DUI": "Q96S44", "7XJN_NSD": "Q9KS37", "7BCP_GCO": "R4JTF7", "7QTA_URI": "P24021", "8EAB_VN2": "P26718", "7PT3_3KK": 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b/notebooks/posebusters_benchmark_mol_to_protein_mapping_2.json new file mode 100644 index 0000000..062491f --- /dev/null +++ b/notebooks/posebusters_benchmark_mol_to_protein_mapping_2.json @@ -0,0 +1 @@ +{"7PK0_BYC": "Q2KIR7", "7ES1_UDP": "Q0DPB7", "7NUT_GLP": "Q9Y303", "7NF0_BYN": "G9NLP8", "7U0U_FK5": "Q4WLV6", "7D6O_MTE": "Unknown", "6VTA_AKN": "Q52424", "7L7C_XQ1": "P0AEG4", "7ZF0_DHR": "Q9SBL1", "7V43_C4O": "A0A088FL33", "6XBO_5MC": "Q61420", "7U3J_L6U": "Q8IVV7", "7OZC_G6S": "Q8A346", "7P1M_4IU": "O00214", "5SIS_JSM": "Q9Y233", "7MFP_Z7P": "Q5UPL1", "7JY3_VUD": "P68871", "7XI7_4RI": "P00374", "7VWF_K55": "Q03181", "7FHA_ADX": "O95340", "7TBU_S3P": "Q5AME2", "7R6J_2I7": "G0SFD1", "6YR2_T1C": "P0ACT4", "7L00_XCJ": "A0A031WIF2", "7VC5_9SF": "A0A7J6JUK2", "7Z7F_IF3": "Q9Y5A9", "7TSF_H4B": "P29476", "7SZA_DUI": "Q96S44", "7XJN_NSD": "Q9KS37", "7BCP_GCO": "R4JTF7", "7QTA_URI": "P24021", "8EAB_VN2": "P26718", "7PT3_3KK": "P0DUV9", "8AIE_M7L": "D5EHC5", "7THI_PGA": "P07738", "8SLG_G5A": 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["6ZC3_JOR"], "P20248": ["7QHL_D5P"], "Q0CMX0": ["7P1F_KFN"], "A0A5S9CYM0": ["8AP0_PRP"], "A0A133CTP6": ["7TB0_UD1"], "A5K1A2": ["6TW5_9M2"], "P00489": ["7Q5I_I0F"], "A0A0F6AY66": ["7N4W_P4V"], "O94804": ["7QGP_DJ8"], "B1MHW4": ["6Z0R_Q4H"], "P29317": ["8BOM_QU6"], "A0A248QE08": ["6YRV_PJ8"], "Q9UZY0": ["7OLI_8HG"], "O00255": ["7UJ4_OQ4"], "Q46085": ["7ZOC_T8E"], "Q4Q5S8": ["6TW7_NZB"], "P10276": ["7WQQ_5Z6"], "P00533": ["8A2D_KXY"], "Q96PN6": ["8CNH_V6U"], "A0A3Q0KSG2": ["7PRI_7TI"], "Q02127": ["6M2B_EZO", "8DHG_T78"], "Q0C8G1": ["7XG5_PLP"], "Q2G1C0": ["6XHT_V2V"], "A0A0H2ZQB9": ["7TXK_LW8"], "P0DW58": ["7UAW_MF6"], "P9WNS3": ["7A9H_TPP"], "Q5SLG6": ["7TH4_FFO"], "K2K6A3": ["7UJ5_DGL"], "A0A2S0PEL5": ["7RC3_SAH"], "P00327": ["7UTW_NAI"], "A0QVP7": ["7ED2_A3P"], "A0A0S4TLR1": ["7P2I_MFU"], "Q6U8B0": ["8CSD_C5P"], "P54577": ["7ROU_66I"], "Q9ZVN4": ["7MOI_HPS"], "P0ABQ4": ["6XG5_TOP"], "B3PEE6": ["7P4C_5OV"], "O49003": ["7PGX_FMN"], "Q08467": ["6Z1C_7EY"], "P00791": ["6XCT_478"], "P29475": ["7TS6_KMI"], "P28720": ["7A9E_R4W"], "Q8CVD0": ["6T88_MWQ"], "A0A3Q7HTY9": ["7Z1Q_NIO"], "Q15562": ["7TYP_KUR"], "P25321": ["7PIH_7QW"], "Q82Y41": ["7C3U_AZG"], "A7RNF3": ["7N7H_CTP"], "Q89Y83": ["7LJN_GTP"], "A0A175WC91": ["7JMV_4NC"], "Q05397": ["6YT6_PKE"], "A0A067E666": ["8AY3_OE3"], "Q15119": ["7VBU_6I4"], "O14757": ["7BJJ_TVW"], "Q92918": ["7R9N_F97", "7SIU_9ID"], "Q16654": ["7EBG_J0L"], "J8G6Z1": ["7UXS_OJC"], "Q9Y4B6": ["8F8E_XJI"], "P0CJ63": ["7L5F_XNG"], "Q9K2N0": ["7AFX_R9K"], "P10275": ["7ZU2_DHT"], "O74036": ["8C3N_ADP"], "C5ZW02": ["7N7B_T3F"], "P0A1E3": ["6Z4N_Q7B"], "O14965": ["7ZTL_BCN"], "P54687": ["7NXO_UU8"], "P68400": ["8AEM_LVF"], "B4RNT1": ["7NU0_DCL"], "A6YEH8": ["7XBV_APC"], "A0A9Y2YA89": ["7V3N_AKG"], "Q8IAR7": ["7ROR_69X"], "Q8ZNC4": ["8D5D_5DK"], "Q9HVZ9": ["8DP2_UMA"], "P25440": ["7USH_82V"], "P80188": ["6Z2C_Q5E"], "P25774": ["6YYO_Q1K"], "P05326": ["6ZAE_ACV"], "F6BL85": ["7DKT_GLF"], "P05413": ["7WPW_F15"], "Q5ZRR1": ["7SDD_4IP"], "Q5ZSZ6": ["7WCF_ACP"], "Q9R9V9": ["8AUH_L9I"]} diff --git a/notebooks/posebusters_benchmark_structure_prediction_chemical_similarity_analysis.ipynb b/notebooks/posebusters_benchmark_structure_prediction_chemical_similarity_analysis.ipynb new file mode 100644 index 0000000..47dcda3 --- /dev/null +++ b/notebooks/posebusters_benchmark_structure_prediction_chemical_similarity_analysis.ipynb @@ -0,0 +1,723 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PoseBusters Benchmark Structure Prediction Chemical Similarity Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import itertools\n", + "import json\n", + "import os\n", + "from collections import defaultdict\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import requests\n", + "import seaborn as sns\n", + "from rdkit import Chem\n", + "from rdkit.Chem import DataStructs\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Configure packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.options.mode.copy_on_write = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define constants" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# General variables\n", + "baseline_methods = [\n", + " # \"vina_p2rank\",\n", + " # \"diffdock\",\n", + " # \"dynamicbind\",\n", + " # \"rfaa\",\n", + " # \"alphafold3\",\n", + " # \"chai-lab\",\n", + " # \"neuralplexer\",\n", + " # \"flowdock_hp\",\n", + " # \"flowdock_aft\",\n", + " # \"flowdock_esmfold\",\n", + " \"flowdock\",\n", + "]\n", + "max_num_repeats_per_method = 3\n", + "\n", + "# Filepaths for each baseline method\n", + "globals()[\"vina_output_dir\"] = os.path.join(\"..\", \"forks\", \"Vina\", \"inference\")\n", + "globals()[\"diffdock_output_dir\"] = os.path.join(\"..\", \"forks\", \"DiffDock\", \"inference\")\n", + "globals()[\"dynamicbind_output_dir\"] = os.path.join(\n", + " \"..\", \"forks\", \"DynamicBind\", \"inference\", \"outputs\", \"results\"\n", + ")\n", + "globals()[\"rfaa_output_dir\"] = os.path.join(\"..\", \"forks\", \"RoseTTAFold-All-Atom\", \"inference\")\n", + "globals()[\"alphafold3_output_dir\"] = os.path.join(\"..\", \"forks\", \"alphafold3\", \"inference\")\n", + "globals()[\"chai-lab_output_dir\"] = os.path.join(\"..\", \"forks\", \"chai-lab\", \"inference\")\n", + "globals()[\"neuralplexer_output_dir\"] = os.path.join(\"..\", \"forks\", \"NeuralPLexer\", \"inference\")\n", + "globals()[\"flowdock_hp_output_dir\"] = os.path.join(\"..\", \"forks\", \"FlowDock\", \"hp_inference\")\n", + "globals()[\"flowdock_aft_output_dir\"] = os.path.join(\"..\", \"forks\", \"FlowDock\", \"aft_inference\")\n", + "globals()[\"flowdock_esmfold_output_dir\"] = os.path.join(\n", + " \"..\", \"forks\", \"FlowDock\", \"esmfold_inference\"\n", + ")\n", + "globals()[\"flowdock_output_dir\"] = os.path.join(\"..\", \"forks\", \"FlowDock\", \"inference\")\n", + "\n", + "for repeat_index in range(1, max_num_repeats_per_method + 1):\n", + " # P2Rank-Vina results\n", + " globals()[f\"vina_p2rank_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n", + " globals()[\"vina_output_dir\"],\n", + " f\"vina_p2rank_posebusters_benchmark_outputs_{repeat_index}\",\n", + " \"bust_results.csv\",\n", + " )\n", + " globals()[f\"vina_p2rank_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n", + " os.path.join(\n", + " globals()[\"vina_output_dir\"],\n", + " f\"vina_p2rank_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n", + " \"bust_results.csv\",\n", + " )\n", + " )\n", + "\n", + " # DiffDock results\n", + " globals()[f\"diffdock_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n", + " globals()[\"diffdock_output_dir\"],\n", + " f\"diffdock_posebusters_benchmark_output_{repeat_index}\",\n", + " \"bust_results.csv\",\n", + " )\n", + " globals()[f\"diffdock_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n", + " os.path.join(\n", + " globals()[\"diffdock_output_dir\"],\n", + " f\"diffdock_posebusters_benchmark_output_{repeat_index}_relaxed\",\n", + " \"bust_results.csv\",\n", + " )\n", + " )\n", + "\n", + " # DynamicBind results\n", + " globals()[f\"dynamicbind_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n", + " globals()[\"dynamicbind_output_dir\"],\n", + " f\"posebusters_benchmark_{repeat_index}\",\n", + " \"bust_results.csv\",\n", + " )\n", + " globals()[f\"dynamicbind_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n", + " os.path.join(\n", + " globals()[\"dynamicbind_output_dir\"],\n", + " f\"posebusters_benchmark_{repeat_index}_relaxed\",\n", + " \"bust_results.csv\",\n", + " )\n", + " )\n", + "\n", + " # RoseTTAFold-All-Atom results\n", + " globals()[f\"rfaa_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n", + " globals()[\"rfaa_output_dir\"],\n", + " f\"rfaa_posebusters_benchmark_outputs_{repeat_index}\",\n", + " \"bust_results.csv\",\n", + " )\n", + " globals()[f\"rfaa_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n", + " globals()[\"rfaa_output_dir\"],\n", + " f\"rfaa_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n", + " \"bust_results.csv\",\n", + " )\n", + "\n", + " # AlphaFold 3 (Single-Seq) results\n", + " globals()[f\"alphafold3_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n", + " globals()[\"alphafold3_output_dir\"],\n", + " f\"alphafold3_ss_posebusters_benchmark_outputs_{repeat_index}\",\n", + " \"bust_results.csv\",\n", + " )\n", + " globals()[f\"alphafold3_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n", + " os.path.join(\n", + " globals()[\"alphafold3_output_dir\"],\n", + " f\"alphafold3_ss_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n", + " \"bust_results.csv\",\n", + " )\n", + " )\n", + "\n", + " # Chai-1 (Single-Seq) results\n", + " globals()[f\"chai-lab_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n", + " globals()[\"chai-lab_output_dir\"],\n", + " f\"chai-lab_ss_posebusters_benchmark_outputs_{repeat_index}\",\n", + " \"bust_results.csv\",\n", + " )\n", + " globals()[f\"chai-lab_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n", + " os.path.join(\n", + " globals()[\"chai-lab_output_dir\"],\n", + " f\"chai-lab_ss_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n", + " \"bust_results.csv\",\n", + " )\n", + " )\n", + "\n", + " # NeuralPLexer results\n", + " globals()[f\"neuralplexer_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n", + " globals()[\"neuralplexer_output_dir\"],\n", + " f\"neuralplexer_posebusters_benchmark_outputs_{repeat_index}\",\n", + " \"bust_results.csv\",\n", + " )\n", + " globals()[f\"neuralplexer_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n", + " os.path.join(\n", + " globals()[\"neuralplexer_output_dir\"],\n", + " f\"neuralplexer_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n", + " \"bust_results.csv\",\n", + " )\n", + " )\n", + "\n", + " # FlowDock-HP results\n", + " globals()[f\"flowdock_hp_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n", + " globals()[\"flowdock_hp_output_dir\"],\n", + " f\"flowdock_posebusters_benchmark_outputs_{repeat_index}\",\n", + " \"bust_results.csv\",\n", + " )\n", + " globals()[f\"flowdock_hp_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n", + " os.path.join(\n", + " globals()[\"flowdock_hp_output_dir\"],\n", + " f\"flowdock_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n", + " \"bust_results.csv\",\n", + " )\n", + " )\n", + "\n", + " # FlowDock-AFT results\n", + " globals()[f\"flowdock_aft_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n", + " globals()[\"flowdock_aft_output_dir\"],\n", + " f\"flowdock_posebusters_benchmark_outputs_{repeat_index}\",\n", + " \"bust_results.csv\",\n", + " )\n", + " globals()[f\"flowdock_aft_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n", + " os.path.join(\n", + " globals()[\"flowdock_aft_output_dir\"],\n", + " f\"flowdock_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n", + " \"bust_results.csv\",\n", + " )\n", + " )\n", + "\n", + " # FlowDock-ESMFold results\n", + " globals()[f\"flowdock_esmfold_posebusters_bust_results_csv_filepath_{repeat_index}\"] = (\n", + " os.path.join(\n", + " globals()[\"flowdock_esmfold_output_dir\"],\n", + " f\"flowdock_posebusters_benchmark_outputs_{repeat_index}\",\n", + " \"bust_results.csv\",\n", + " )\n", + " )\n", + " globals()[f\"flowdock_esmfold_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n", + " os.path.join(\n", + " globals()[\"flowdock_esmfold_output_dir\"],\n", + " f\"flowdock_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n", + " \"bust_results.csv\",\n", + " )\n", + " )\n", + "\n", + " # FlowDock results\n", + " globals()[f\"flowdock_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n", + " globals()[\"flowdock_output_dir\"],\n", + " f\"flowdock_posebusters_benchmark_outputs_{repeat_index}\",\n", + " \"bust_results.csv\",\n", + " )\n", + " globals()[f\"flowdock_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n", + " os.path.join(\n", + " globals()[\"flowdock_output_dir\"],\n", + " f\"flowdock_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n", + " \"bust_results.csv\",\n", + " )\n", + " )\n", + "\n", + "# Mappings\n", + "method_mapping = {\n", + " \"vina_p2rank\": \"P2Rank-Vina\",\n", + " \"diffdock\": \"DiffDock-L\",\n", + " \"dynamicbind\": \"DynamicBind\",\n", + " \"rfaa\": \"RoseTTAFold-AA\",\n", + " \"alphafold3\": \"AF3-Single-Seq\",\n", + " \"chai-lab\": \"Chai-1-Single-Seq\",\n", + " \"neuralplexer\": \"NeuralPLexer\",\n", + " \"flowdock_hp\": \"FlowDock-HP\",\n", + " \"flowdock_aft\": \"FlowDock-AFT\",\n", + " \"flowdock_esmfold\": \"FlowDock-ESMFold\",\n", + " \"flowdock\": \"FlowDock-AF3\",\n", + "}\n", + "\n", + "method_category_mapping = {\n", + " \"vina_p2rank\": \"Conventional blind\",\n", + " \"diffdock\": \"DL-based blind\",\n", + " \"dynamicbind\": \"DL-based blind\",\n", + " \"rfaa\": \"DL-based blind\",\n", + " \"alphafold3\": \"DL-based blind\",\n", + " \"chai-lab\": \"DL-based blind\",\n", + " \"neuralplexer\": \"DL-based blind\",\n", + " \"flowdock_hp\": \"DL-based blind\",\n", + " \"flowdock_aft\": \"DL-based blind\",\n", + " \"flowdock_esmfold\": \"DL-based blind\",\n", + " \"flowdock\": \"DL-based blind\",\n", + "}\n", + "\n", + "# Metrics\n", + "BUST_TEST_COLUMNS = [\n", + " # accuracy #\n", + " \"rmsd_≤_2å\",\n", + " # chemical validity and consistency #\n", + " \"mol_pred_loaded\",\n", + " \"mol_true_loaded\",\n", + " \"mol_cond_loaded\",\n", + " \"sanitization\",\n", + " \"molecular_formula\",\n", + " \"molecular_bonds\",\n", + " \"tetrahedral_chirality\",\n", + " \"double_bond_stereochemistry\",\n", + " # intramolecular validity #\n", + " \"bond_lengths\",\n", + " \"bond_angles\",\n", + " \"internal_steric_clash\",\n", + " \"aromatic_ring_flatness\",\n", + " \"double_bond_flatness\",\n", + " \"internal_energy\",\n", + " # intermolecular validity #\n", + " \"minimum_distance_to_protein\",\n", + " \"minimum_distance_to_organic_cofactors\",\n", + " \"minimum_distance_to_inorganic_cofactors\",\n", + " \"volume_overlap_with_protein\",\n", + " \"volume_overlap_with_organic_cofactors\",\n", + " \"volume_overlap_with_inorganic_cofactors\",\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Report test results for each baseline method" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load and report test results for each baseline method\n", + "for config in [\"\", \"_relaxed\"]:\n", + " for method in baseline_methods:\n", + " for repeat_index in range(1, max_num_repeats_per_method + 1):\n", + " method_title = method_mapping[method]\n", + "\n", + " if not os.path.exists(\n", + " globals()[f\"{method}_posebusters{config}_bust_results_csv_filepath_{repeat_index}\"]\n", + " ):\n", + " continue\n", + "\n", + " globals()[f\"{method}_posebusters{config}_bust_results_{repeat_index}\"] = pd.read_csv(\n", + " globals()[f\"{method}_posebusters{config}_bust_results_csv_filepath_{repeat_index}\"]\n", + " )\n", + " assert (\n", + " \"mol_id\"\n", + " in globals()[f\"{method}_posebusters{config}_bust_results_{repeat_index}\"].columns\n", + " ), f\"mol_id column not found in {method_title}{config} bust_results CSV file for repeat {repeat_index}\"\n", + " globals()[\n", + " f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"\n", + " ] = globals()[f\"{method}_posebusters{config}_bust_results_{repeat_index}\"][\n", + " BUST_TEST_COLUMNS + [\"rmsd\", \"mol_id\", \"inchi_crystal\"]\n", + " ]\n", + " globals()[f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"].loc[\n", + " :, \"pb_valid\"\n", + " ] = (\n", + " globals()[f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"]\n", + " .iloc[:, 1:-3]\n", + " .all(axis=1)\n", + " )\n", + "\n", + " globals()[f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"].loc[\n", + " :, \"method\"\n", + " ] = method\n", + " globals()[f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"].loc[\n", + " :, \"post-processing\"\n", + " ] = (\"energy minimization\" if config == \"_relaxed\" else \"none\")\n", + " globals()[f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"].loc[\n", + " :, \"dataset\"\n", + " ] = \"posebusters\"\n", + " globals()[f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"].loc[\n", + " :, \"docked_ligand_successfully_loaded\"\n", + " ] = globals()[f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"][\n", + " [\"mol_pred_loaded\", \"mol_true_loaded\", \"mol_cond_loaded\"]\n", + " ].all(\n", + " axis=1\n", + " )\n", + "\n", + " globals()[f\"{method}{config}_bust_results_table_{repeat_index}\"] = globals()[\n", + " f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"\n", + " ]\n", + "\n", + " print(\n", + " f\"{method_title}{config}_{repeat_index} PoseBusters Benchmark set `rmsd_≤_2å`: {globals()[f'{method}_posebusters{config}_bust_results_table_{repeat_index}']['rmsd_≤_2å'].mean()}\"\n", + " )\n", + " print(\n", + " f\"{method_title}{config}_{repeat_index} PoseBusters Benchmark set `rmsd_≤_2å and pb_valid`: {globals()[f'{method}_posebusters{config}_bust_results_table_{repeat_index}'][globals()[f'{method}_posebusters{config}_bust_results_table_{repeat_index}']['pb_valid']]['rmsd_≤_2å'].sum() / len(globals()[f'{method}_posebusters{config}_bust_results_table_{repeat_index}'])}\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def assign_method_index(method: str) -> str:\n", + " \"\"\"\n", + " Assign method index for plotting.\n", + "\n", + " :param method: Method name.\n", + " :return: Method index.\n", + " \"\"\"\n", + " return list(method_mapping.keys()).index(method)\n", + "\n", + "\n", + "def categorize_method(method: str) -> str:\n", + " \"\"\"\n", + " Categorize method for plotting.\n", + "\n", + " :param method: Method name.\n", + " :return: Method category.\n", + " \"\"\"\n", + " return method_category_mapping.get(method, \"Misc\")\n", + "\n", + "\n", + "def get_uniprot_mapping(pdb_id: str) -> set:\n", + " \"\"\"\n", + " Get the UniProt mapping for a given PDB ID using the PDBe API.\n", + "\n", + " :param pdb_id: PDB ID (e.g., \"1A2B\").\n", + " :return: Set of UniProt IDs mapped to the PDB ID, or None if not found.\n", + " \"\"\"\n", + " # use the PDBe API to get the mapping data\n", + " url = f\"https://www.ebi.ac.uk/pdbe/api/mappings/uniprot/{pdb_id.lower()}\"\n", + " response = requests.get(url)\n", + " if response.status_code != 200:\n", + " return None\n", + " data = response.json()\n", + " uniprot_ids = set()\n", + " if pdb_id.lower() in data:\n", + " # NOTE: each mapping corresponds to one chain/segment mapped to a UniProt entry\n", + " for uniprot_id in data[pdb_id.lower()].get(\"UniProt\", []):\n", + " uniprot_ids.add(uniprot_id)\n", + " return uniprot_ids\n", + "\n", + "\n", + "def group_has_low_similarity(group: pd.DataFrame, threshold: float = 0.6) -> bool:\n", + " \"\"\"\n", + " Return True if every pair of molecules in the group has Tanimoto similarity less than threshold.\n", + " Groups with only one ligand are considered valid.\n", + "\n", + " :param group: DataFrame group containing the fingerprints.\n", + " :param threshold: Similarity threshold for filtering.\n", + " :return: True if the group has low similarity, False otherwise.\n", + " \"\"\"\n", + " max_sim = 0.0\n", + " fps = group[\"fp\"].tolist()\n", + " if len(fps) < 2:\n", + " group[\"max_fp_sim\"] = max_sim\n", + " return True\n", + " for fp1, fp2 in itertools.combinations(fps, 2):\n", + " # Only calculate similarity if both fingerprints are valid.\n", + " if fp1 is not None and fp2 is not None:\n", + " sim = DataStructs.FingerprintSimilarity(fp1, fp2)\n", + " max_sim = max(max_sim, sim)\n", + " if sim >= threshold:\n", + " return False\n", + " group[\"max_fp_sim\"] = max_sim\n", + " return True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Standardize metrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load and organize the PoseBusters Benchmark results CSVs\n", + "for repeat_index in range(1, max_num_repeats_per_method + 1):\n", + " globals()[f\"results_table_{repeat_index}\"] = pd.concat(\n", + " [\n", + " globals()[f\"{method}{config}_bust_results_table_{repeat_index}\"]\n", + " for method in baseline_methods\n", + " for config in [\"\", \"_relaxed\"]\n", + " if f\"{method}{config}_bust_results_table_{repeat_index}\" in globals()\n", + " ]\n", + " )\n", + " globals()[f\"results_table_{repeat_index}\"].loc[:, \"method_category\"] = globals()[\n", + " f\"results_table_{repeat_index}\"\n", + " ][\"method\"].apply(categorize_method)\n", + " globals()[f\"results_table_{repeat_index}\"].loc[:, \"method_assignment_index\"] = globals()[\n", + " f\"results_table_{repeat_index}\"\n", + " ][\"method\"].apply(assign_method_index)\n", + " globals()[f\"results_table_{repeat_index}\"].loc[:, \"rmsd_within_threshold\"] = (\n", + " globals()[f\"results_table_{repeat_index}\"].loc[:, \"rmsd_≤_2å\"].fillna(False)\n", + " )\n", + " globals()[f\"results_table_{repeat_index}\"].loc[:, \"rmsd_within_threshold_and_pb_valid\"] = (\n", + " globals()[f\"results_table_{repeat_index}\"].loc[:, \"rmsd_within_threshold\"]\n", + " ) & (globals()[f\"results_table_{repeat_index}\"].loc[:, \"pb_valid\"].fillna(False))\n", + " globals()[f\"results_table_{repeat_index}\"].loc[:, \"RMSD ≤ 2 Å & PB-Valid\"] = (\n", + " globals()[f\"results_table_{repeat_index}\"]\n", + " .loc[:, \"rmsd_within_threshold_and_pb_valid\"]\n", + " .astype(int)\n", + " )\n", + " globals()[f\"results_table_{repeat_index}\"].loc[:, \"RMSD ≤ 2 Å\"] = (\n", + " globals()[f\"results_table_{repeat_index}\"]\n", + " .loc[:, \"rmsd_within_threshold\"]\n", + " .fillna(False)\n", + " .astype(int)\n", + " )\n", + " globals()[f\"results_table_{repeat_index}\"].loc[:, \"dataset\"] = (\n", + " globals()[f\"results_table_{repeat_index}\"]\n", + " .loc[:, \"dataset\"]\n", + " .map({\"posebusters\": \"PoseBusters Benchmark set\"})\n", + " )\n", + " globals()[f\"results_table_{repeat_index}\"].loc[:, \"method\"] = (\n", + " globals()[f\"results_table_{repeat_index}\"].loc[:, \"method\"].map(method_mapping)\n", + " )\n", + "\n", + " # group ligands according to their binding proteins (here defined as their first UniProt IDs, if available)\n", + " mol_to_protein_mapping = dict()\n", + " protein_grouped_mols = defaultdict(list)\n", + " mol_ids = list(set(globals()[f\"results_table_{repeat_index}\"].mol_id))\n", + "\n", + " protein_grouped_mols_json_filepath = (\n", + " f\"posebusters_benchmark_protein_grouped_mols_{repeat_index}.json\"\n", + " )\n", + " mol_to_protein_mapping_json_filepath = (\n", + " f\"posebusters_benchmark_mol_to_protein_mapping_{repeat_index}.json\"\n", + " )\n", + "\n", + " if os.path.exists(protein_grouped_mols_json_filepath):\n", + " # load cached groupings if available\n", + " with open(protein_grouped_mols_json_filepath, \"r\") as f:\n", + " protein_grouped_mols = json.load(f)\n", + " if os.path.exists(mol_to_protein_mapping_json_filepath):\n", + " # load cached mappings if available\n", + " with open(mol_to_protein_mapping_json_filepath, \"r\") as f:\n", + " mol_to_protein_mapping = json.load(f)\n", + "\n", + " if not (protein_grouped_mols and mol_to_protein_mapping):\n", + " for mol in tqdm(mol_ids, desc=\"Grouping ligands by protein\"):\n", + " pdb_id = mol.split(\"_\")[0]\n", + " uniprot_ids = get_uniprot_mapping(pdb_id)\n", + "\n", + " group_uniprot_id = None\n", + "\n", + " if uniprot_ids:\n", + " for uniprot_id in uniprot_ids:\n", + " if uniprot_id in protein_grouped_mols:\n", + " group_uniprot_id = uniprot_id\n", + " protein_grouped_mols[group_uniprot_id].append(mol)\n", + " break\n", + "\n", + " if not group_uniprot_id:\n", + " group_uniprot_id = list(sorted(uniprot_ids))[0]\n", + " protein_grouped_mols[group_uniprot_id] = [mol]\n", + " else:\n", + " group_uniprot_id = \"Unknown\"\n", + "\n", + " mol_to_protein_mapping[mol] = group_uniprot_id\n", + "\n", + " # cache groupings\n", + " with open(protein_grouped_mols_json_filepath, \"w\") as f:\n", + " json.dump(protein_grouped_mols, f)\n", + " with open(mol_to_protein_mapping_json_filepath, \"w\") as f:\n", + " json.dump(mol_to_protein_mapping, f)\n", + "\n", + " # add the protein group IDs to the results table\n", + " globals()[f\"results_table_{repeat_index}\"].loc[:, \"protein_id\"] = globals()[\n", + " f\"results_table_{repeat_index}\"\n", + " ][\"mol_id\"].map(mol_to_protein_mapping)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Make plots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# RMSD Violin Plot of PoseBusters Benchmark Set Results (Relaxed vs. Unrelaxed and Grouped Per Protein) #\n", + "\n", + "# prepare data for the violin plots to plot\n", + "colors = [\"#FB8072\", \"#BEBADA\"]\n", + "\n", + "# combine results for each dataset across all three repeats\n", + "combined_data_list = []\n", + "for repeat_index in range(1, max_num_repeats_per_method + 1):\n", + " ad_relaxed_results_table = globals()[f\"results_table_{repeat_index}\"][\n", + " (globals()[f\"results_table_{repeat_index}\"][\"dataset\"] == \"PoseBusters Benchmark set\")\n", + " & (globals()[f\"results_table_{repeat_index}\"][\"post-processing\"] == \"energy minimization\")\n", + " & (globals()[f\"results_table_{repeat_index}\"][\"protein_id\"] != \"Unknown\")\n", + " ]\n", + " ad_relaxed_results_table.loc[\n", + " :, \"protein_group_size\"\n", + " ] = ad_relaxed_results_table.protein_id.value_counts()[\n", + " ad_relaxed_results_table.protein_id\n", + " ].values\n", + " ad_relaxed_results_table = ad_relaxed_results_table[\n", + " ad_relaxed_results_table.protein_group_size > 1\n", + " ].reset_index() # filter out singleton protein groups\n", + "\n", + " ad_relaxed_results_table[\"mol\"] = ad_relaxed_results_table[\"inchi_crystal\"].apply(\n", + " lambda x: Chem.MolFromInchi(x)\n", + " )\n", + " ad_relaxed_results_table[\"fp\"] = ad_relaxed_results_table[\"mol\"].apply(\n", + " lambda m: Chem.RDKFingerprint(m) if m is not None else None\n", + " )\n", + " ad_relaxed_results_table = pd.concat(\n", + " [\n", + " group\n", + " for _, group in ad_relaxed_results_table.groupby(\"protein_id\")\n", + " if group_has_low_similarity(group, threshold=0.6)\n", + " ]\n", + " )\n", + "\n", + " ad_unrelaxed_results_table = globals()[f\"results_table_{repeat_index}\"][\n", + " (globals()[f\"results_table_{repeat_index}\"][\"dataset\"] == \"PoseBusters Benchmark set\")\n", + " & (globals()[f\"results_table_{repeat_index}\"][\"post-processing\"] == \"none\")\n", + " & (globals()[f\"results_table_{repeat_index}\"][\"protein_id\"] != \"Unknown\")\n", + " ]\n", + " ad_unrelaxed_results_table.loc[\n", + " :, \"protein_group_size\"\n", + " ] = ad_unrelaxed_results_table.protein_id.value_counts()[\n", + " ad_unrelaxed_results_table.protein_id\n", + " ].values\n", + " ad_unrelaxed_results_table = ad_unrelaxed_results_table[\n", + " ad_unrelaxed_results_table.protein_group_size > 1\n", + " ].reset_index() # filter out singleton protein groups\n", + "\n", + " ad_unrelaxed_results_table[\"mol\"] = ad_unrelaxed_results_table[\"inchi_crystal\"].apply(\n", + " lambda x: Chem.MolFromInchi(x)\n", + " )\n", + " ad_unrelaxed_results_table[\"fp\"] = ad_unrelaxed_results_table[\"mol\"].apply(\n", + " lambda m: Chem.RDKFingerprint(m) if m is not None else None\n", + " )\n", + " ad_unrelaxed_results_table = pd.concat(\n", + " [\n", + " group\n", + " for _, group in ad_unrelaxed_results_table.groupby(\"protein_id\")\n", + " if group_has_low_similarity(group, threshold=0.6)\n", + " ]\n", + " )\n", + "\n", + " combined_data_list.append(pd.concat([ad_relaxed_results_table, ad_unrelaxed_results_table]))\n", + "\n", + "combined_relaxed_data = (\n", + " pd.concat(combined_data_list).sort_values(\"method_assignment_index\").reset_index(drop=True)\n", + ")\n", + "\n", + "assert (\n", + " len(combined_relaxed_data.method.unique()) == 1\n", + "), f\"Expected only one method in the combined data, but found: {combined_relaxed_data.method.unique()}\"\n", + "\n", + "# define font properties\n", + "plt.rcParams[\"font.size\"] = 12\n", + "plt.rcParams[\"axes.labelsize\"] = 14\n", + "\n", + "# set the size of the figure\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# create a violin plot grouped by protein_id\n", + "sns.violinplot(\n", + " x=\"protein_id\",\n", + " y=\"rmsd\",\n", + " hue=\"post-processing\",\n", + " # ignore outliers for better readability\n", + " data=combined_relaxed_data[combined_relaxed_data[\"rmsd\"] < 10],\n", + " split=True,\n", + " inner=\"quartile\",\n", + " palette=colors,\n", + " cut=0,\n", + ")\n", + "\n", + "# set labels and title\n", + "plt.xlabel(\"UniProt ID\")\n", + "plt.ylabel(\"RMSD\")\n", + "\n", + "# rotate x-axis labels for better readability\n", + "plt.xticks(rotation=45, ha=\"right\")\n", + "\n", + "# display legend outside the plot\n", + "plt.legend(title=\"Post-processing\", bbox_to_anchor=(1.05, 1), loc=\"best\")\n", + "\n", + "# display the plots\n", + "plt.tight_layout()\n", + "plt.savefig(\n", + " \"posebusters_benchmark_structure_prediction_relaxed_rmsd_violin_plot_by_protein.png\", dpi=300\n", + ")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "FlowDock", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/posebusters_benchmark_structure_prediction_relaxed_bar_chart.png b/notebooks/posebusters_benchmark_structure_prediction_relaxed_bar_chart.png index 6c2c239..2f894eb 100644 Binary files a/notebooks/posebusters_benchmark_structure_prediction_relaxed_bar_chart.png and b/notebooks/posebusters_benchmark_structure_prediction_relaxed_bar_chart.png differ diff --git a/notebooks/posebusters_benchmark_structure_prediction_relaxed_rmsd_violin_plot.png b/notebooks/posebusters_benchmark_structure_prediction_relaxed_rmsd_violin_plot.png index 0cfb4c4..139362c 100644 Binary files a/notebooks/posebusters_benchmark_structure_prediction_relaxed_rmsd_violin_plot.png and b/notebooks/posebusters_benchmark_structure_prediction_relaxed_rmsd_violin_plot.png differ diff --git a/notebooks/posebusters_benchmark_structure_prediction_relaxed_rmsd_violin_plot_by_protein.png b/notebooks/posebusters_benchmark_structure_prediction_relaxed_rmsd_violin_plot_by_protein.png new file mode 100644 index 0000000..883aeca Binary files /dev/null and b/notebooks/posebusters_benchmark_structure_prediction_relaxed_rmsd_violin_plot_by_protein.png differ diff --git a/notebooks/posebusters_benchmark_structure_prediction_results_plotting.ipynb b/notebooks/posebusters_benchmark_structure_prediction_results_plotting.ipynb index 46ec209..0c4f9b9 100644 --- a/notebooks/posebusters_benchmark_structure_prediction_results_plotting.ipynb +++ b/notebooks/posebusters_benchmark_structure_prediction_results_plotting.ipynb @@ -69,6 +69,7 @@ " \"flowdock_hp\",\n", " \"flowdock_aft\",\n", " \"flowdock_esmfold\",\n", + " \"flowdock_chai\",\n", " \"flowdock\",\n", "]\n", "max_num_repeats_per_method = 3\n", @@ -88,6 +89,7 @@ "globals()[\"flowdock_esmfold_output_dir\"] = os.path.join(\n", " \"..\", \"forks\", \"FlowDock\", \"esmfold_inference\"\n", ")\n", + "globals()[\"flowdock_chai_output_dir\"] = os.path.join(\"..\", \"forks\", \"FlowDock\", \"chai_inference\")\n", "globals()[\"flowdock_output_dir\"] = os.path.join(\"..\", \"forks\", \"FlowDock\", \"inference\")\n", "\n", "for repeat_index in range(1, max_num_repeats_per_method + 1):\n", @@ -231,6 +233,22 @@ " )\n", " )\n", "\n", + " # FlowDock-Chai results\n", + " globals()[f\"flowdock_chai_posebusters_bust_results_csv_filepath_{repeat_index}\"] = (\n", + " os.path.join(\n", + " globals()[\"flowdock_chai_output_dir\"],\n", + " f\"flowdock_posebusters_benchmark_outputs_{repeat_index}\",\n", + " \"bust_results.csv\",\n", + " )\n", + " )\n", + " globals()[f\"flowdock_chai_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n", + " os.path.join(\n", + " globals()[\"flowdock_chai_output_dir\"],\n", + " f\"flowdock_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n", + " \"bust_results.csv\",\n", + " )\n", + " )\n", + "\n", " # FlowDock results\n", " globals()[f\"flowdock_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n", " globals()[\"flowdock_output_dir\"],\n", @@ -257,6 +275,7 @@ " \"flowdock_hp\": \"FlowDock-HP\",\n", " \"flowdock_aft\": \"FlowDock-AFT\",\n", " \"flowdock_esmfold\": \"FlowDock-ESMFold\",\n", + " \"flowdock_chai\": \"FlowDock-Chai-1\",\n", " \"flowdock\": \"FlowDock-AF3\",\n", "}\n", "\n", @@ -271,6 +290,7 @@ " \"flowdock_hp\": \"DL-based blind\",\n", " \"flowdock_aft\": \"DL-based blind\",\n", " \"flowdock_esmfold\": \"DL-based blind\",\n", + " \"flowdock_chai\": \"DL-based blind\",\n", " \"flowdock\": \"DL-based blind\",\n", "}\n", "\n", @@ -544,7 +564,7 @@ "colors = [\"#FB8072\", \"#BEBADA\"]\n", "\n", "bar_width = 0.5\n", - "r1 = [item - 0.25 for item in range(2, 24, 2)]\n", + "r1 = [item - 0.25 for item in range(2, 26, 2)]\n", "r2 = [x + bar_width for x in r1]\n", "\n", "(\n", @@ -736,8 +756,8 @@ "posebusters_relaxed_rmsd_lt_2_and_pb_valid_data_std.fillna(0, inplace=True)\n", "\n", "# define font properties\n", - "plt.rcParams[\"font.size\"] = 24\n", - "plt.rcParams[\"axes.labelsize\"] = 26\n", + "plt.rcParams[\"font.size\"] = 20\n", + "plt.rcParams[\"axes.labelsize\"] = 22\n", "\n", "# create the figure and a list of axes\n", "fig, axis = plt.subplots(figsize=(34, 14))\n", @@ -788,7 +808,7 @@ "\n", "# add labels, titles, ticks, etc.\n", "axis.set_ylabel(\"Percentage of predictions\")\n", - "axis.set_xlim(1, 23 + 0.1)\n", + "axis.set_xlim(1, 25 + 0.1)\n", "axis.set_ylim(0, 125)\n", "\n", "assert len(posebusters_rmsd_lt_2_bar) == len(posebusters_rmsd_lt_2_and_pb_valid_bar), (\n", @@ -861,7 +881,7 @@ "axis.grid(axis=\"y\", color=\"#EAEFF8\")\n", "axis.set_axisbelow(True)\n", "\n", - "axis.set_xticks([2, 2 + 1e-3, 4, 6, 8, 10, 12, 13, 14, 16, 18, 20, 22])\n", + "axis.set_xticks([2, 2 + 1e-3, 4, 6, 8, 10, 12, 14, 14 + 1e-3, 16, 18, 20, 22, 24])\n", "axis.set_xticks([1 + 0.1], minor=True)\n", "axis.set_xticklabels(\n", " [\n", @@ -872,11 +892,12 @@ " \"RoseTTAFold-AA\",\n", " \"AF3-Single-Seq\",\n", " \"Chai-1-Single-Seq\",\n", - " \"DL-based blind\",\n", " \"NeuralPLexer\",\n", + " \"DL-based blind\",\n", " \"FlowDock-HP\",\n", " \"FlowDock-AFT\",\n", " \"FlowDock-ESMFold\",\n", + " \"FlowDock-Chai-1\",\n", " \"FlowDock-AF3\",\n", " ]\n", ")\n", @@ -889,7 +910,7 @@ "axis.tick_params(axis=\"y\", which=\"major\", left=\"off\", right=\"on\", color=\"#EAEFF8\")\n", "\n", "# vertical alignment of xtick labels\n", - "vert_alignments = [0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0]\n", + "vert_alignments = [0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0]\n", "for tick, y in zip(axis.get_xticklabels(), vert_alignments):\n", " tick.set_y(y)\n", "\n", @@ -934,7 +955,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.17" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/scripts/esmfold_prior_plinder_finetuning.sh b/scripts/esmfold_prior_plinder_finetuning.sh new file mode 100644 index 0000000..172344e --- /dev/null +++ b/scripts/esmfold_prior_plinder_finetuning.sh @@ -0,0 +1,64 @@ +#!/bin/bash -l +######################### Batch Headers ######################### +#SBATCH --partition chengji-lab-gpu # NOTE: use reserved partition `chengji-lab-gpu` to use reserved A100 or H100 GPUs +#SBATCH --account chengji-lab # NOTE: this must be specified to use the reserved partition above +#SBATCH --nodes=1 # NOTE: this needs to match Lightning's `Trainer(num_nodes=...)` +#SBATCH --gres gpu:1 # request A100/H100 GPU resource(s) +#SBATCH --ntasks-per-node=1 # NOTE: this needs to be `1` on SLURM clusters when using Lightning's `ddp_spawn` strategy`; otherwise, set to match Lightning's quantity of `Trainer(devices=...)` +#SBATCH --mem=59G # NOTE: use `--mem=0` to request all memory "available" on the assigned node +#SBATCH -t 2-00:00:00 # time limit for the job (up to 2 days: `2-00:00:00`) +#SBATCH -J esmfold_prior_plinder_finetuning # job name +#SBATCH --output=R-%x.%j.out # output log file +#SBATCH --error=R-%x.%j.err # error log file + +module purge +module load cuda/11.8.0_gcc_9.5.0 + +# determine location of the project directory +use_private_project_dir=false # NOTE: customize as needed +if [ "$use_private_project_dir" = true ]; then + project_dir="/home/acmwhb/data/Repositories/Lab_Repositories/FlowDock" +else + project_dir="/cluster/pixstor/chengji-lab/acmwhb/Repositories/Lab_Repositories/FlowDock" +fi + +# shellcheck source=/dev/null +source /cluster/pixstor/chengji-lab/acmwhb/miniforge3/etc/profile.d/conda.sh +conda activate "$project_dir"/FlowDock/ + +# Reference Conda system libraries +export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$LD_LIBRARY_PATH" + +echo "Calling flowdock/train.py!" +cd "$project_dir" || exit +srun python3 flowdock/train.py \ + callbacks.last_model_checkpoint.filename=null \ + callbacks.last_model_checkpoint.every_n_train_steps=200 \ + callbacks.last_model_checkpoint.every_n_epochs=null \ + ckpt_path="$(realpath 'logs/train/runs/2025-03-17_17-39-39/checkpoints/169-562000.ckpt')" \ + data=plinder \ + experiment='flowdock_fm' \ + environment=slurm \ + logger=wandb \ + logger.wandb.entity='bml-lab' \ + logger.wandb.group='FlowDock-FM' \ + +logger.wandb.name='2025-03-17_17:00:00-ESMFold-Prior-PLINDER-Finetuning' \ + +logger.wandb.id='1x2k5a79' \ + model.cfg.prior_type=esmfold \ + model.cfg.task.freeze_score_head=false \ + model.cfg.task.freeze_affinity=true \ + paths.output_dir="$(realpath 'logs/train/runs/2025-03-17_17-39-39')" \ + strategy=ddp \ + trainer=ddp \ + +trainer.accumulate_grad_batches=4 \ + trainer.devices=1 \ + trainer.num_nodes=1 +echo "Finished calling flowdock/train.py!" + +# NOTE: the following commands must be used to resume training from a checkpoint +# ckpt_path="$(realpath 'logs/train/runs/2025-03-17_17-39-39/checkpoints/169-562000.ckpt')" \ +# paths.output_dir="$(realpath 'logs/train/runs/2025-03-17_17-39-39')" \ + +# NOTE: the following commands may be used to speed up training +# model.compile=false \ +# +trainer.precision=bf16-mixed diff --git a/scripts/esmfold_prior_tiered_training.sh b/scripts/esmfold_prior_tiered_training.sh index bc71a6c..2593faf 100644 --- a/scripts/esmfold_prior_tiered_training.sh +++ b/scripts/esmfold_prior_tiered_training.sh @@ -30,6 +30,9 @@ fi source /cluster/pixstor/chengji-lab/acmwhb/miniforge3/etc/profile.d/conda.sh conda activate "$project_dir"/FlowDock/ +# Reference Conda system libraries +export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$LD_LIBRARY_PATH" + # NOTE: for tiered training, start by setting `model.cfg.task.freeze_score_head=false` and `model.cfg.task.freeze_affinity=true`, # and once the model's score head has been trained to convergence, resume training with `model.cfg.task.freeze_score_head=true` and `model.cfg.task.freeze_affinity=false` diff --git a/scripts/esmfold_prior_training.sh b/scripts/esmfold_prior_training.sh index b6d88ac..6719383 100644 --- a/scripts/esmfold_prior_training.sh +++ b/scripts/esmfold_prior_training.sh @@ -30,6 +30,9 @@ fi source /cluster/pixstor/chengji-lab/acmwhb/miniforge3/etc/profile.d/conda.sh conda activate "$project_dir"/FlowDock/ +# Reference Conda system libraries +export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$LD_LIBRARY_PATH" + echo "Calling flowdock/train.py!" cd "$project_dir" || exit srun python3 flowdock/train.py \ diff --git a/scripts/harmonic_prior_training.sh b/scripts/harmonic_prior_training.sh index b6cc386..f7e05c2 100644 --- a/scripts/harmonic_prior_training.sh +++ b/scripts/harmonic_prior_training.sh @@ -30,6 +30,9 @@ fi source /cluster/pixstor/chengji-lab/acmwhb/miniforge3/etc/profile.d/conda.sh conda activate "$project_dir"/FlowDock/ +# Reference Conda system libraries +export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$LD_LIBRARY_PATH" + echo "Calling flowdock/train.py!" cd "$project_dir" || exit srun python3 flowdock/train.py \ diff --git a/scripts/plinder_download.sh b/scripts/plinder_download.sh new file mode 100644 index 0000000..b117b73 --- /dev/null +++ b/scripts/plinder_download.sh @@ -0,0 +1,38 @@ +#!/bin/bash -l +######################### Batch Headers ######################### +#SBATCH --partition general # NOTE: use reserved partition `chengji-lab-gpu` to use reserved A100 or H100 GPUs +#SBATCH --account chengji-lab # NOTE: this must be specified to use the reserved partition above +#SBATCH --nodes=1 # NOTE: this needs to match Lightning's `Trainer(num_nodes=...)` +#SBATCH --ntasks-per-node=1 # NOTE: this needs to be `1` on SLURM clusters when using Lightning's `ddp_spawn` strategy`; otherwise, set to match Lightning's quantity of `Trainer(devices=...)` +#SBATCH --mem=59G # NOTE: use `--mem=0` to request all memory "available" on the assigned node +#SBATCH -t 0-02:00:00 # time limit for the job (up to 2 days: `2-00:00:00`) +#SBATCH -J plinder_download # job name +#SBATCH --output=R-%x.%j.out # output log file +#SBATCH --error=R-%x.%j.err # error log file + +module purge +module load cuda/11.8.0_gcc_9.5.0 + +# determine location of the project directory +use_private_project_dir=false # NOTE: customize as needed +if [ "$use_private_project_dir" = true ]; then + project_dir="/home/acmwhb/data/Repositories/Lab_Repositories/FlowDock" +else + project_dir="/cluster/pixstor/chengji-lab/acmwhb/Repositories/Lab_Repositories/FlowDock" +fi + +# shellcheck source=/dev/null +source /cluster/pixstor/chengji-lab/acmwhb/miniforge3/etc/profile.d/conda.sh +conda activate "$project_dir"/FlowDock/ + +# Reference Conda system libraries +export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$LD_LIBRARY_PATH" + +# determine location of PLINDER dataset +export PLINDER_MOUNT="$project_dir/data/PLINDER" # NOTE: customize as needed +mkdir -p "$PLINDER_MOUNT" # create the directory if it doesn't exist + +echo "Downloading PLINDER to $PLINDER_MOUNT!" +cd "$project_dir" || exit +plinder_download -y +echo "Finished downloading PLINDER to $PLINDER_MOUNT!"