Comprehensive benchmarking of protein-ligand structure prediction methods
- Installation
- Tutorials
- How to prepare PoseBench data
- Available inference methods
- How to run a sweep of benchmarking experiments
- How to run inference with individual methods
- How to run inference with a method ensemble
- How to create comparative plots of inference results
- For developers
- Acknowledgements
- Citing this work
- Bonus
To reuse modules and utilities within PoseBench in other projects, one can simply use pip
pip install posebenchTo reproduce, customize, or extend the PoseBench benchmark, we recommend fully installing PoseBench using mamba as follows:
First, install mamba for dependency management (as a fast alternative to Anaconda)
wget "https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-$(uname)-$(uname -m).sh"
bash Mambaforge-$(uname)-$(uname -m).sh # accept all terms and install to the default location
rm Mambaforge-$(uname)-$(uname -m).sh # (optionally) remove installer after using it
source ~/.bashrc # alternatively, one can restart their shell session to achieve the same resultInstall dependencies for each method's environment (as desired)
# clone project
sudo apt-get install git-lfs # NOTE: run this if you have not already installed `git-lfs`
git lfs install
git clone https://github.com/BioinfoMachineLearning/PoseBench --recursive
cd PoseBench
# create conda environments (~80 GB total)
# - PoseBench environment # (~15 GB)
mamba env create -f environments/posebench_environment.yaml
conda activate PoseBench # NOTE: one still needs to use `conda` to (de)activate environments
pip3 install -e .
pip3 install numpy==1.26.4 --no-dependencies
pip3 install prody==2.4.1 --no-dependencies
# - PyMOL environment # (~1 GB)
mamba env create -f environments/pymol_environment.yaml
conda activate PyMOL-PoseBench
pip install -e . --no-deps
# - casp15_ligand_scoring environment (~3 GB)
mamba env create -f environments/casp15_ligand_scoring_environment.yaml
conda activate casp15_ligand_scoring # NOTE: one still needs to use `conda` to (de)activate environments
pip3 install -e .
# - DiffDock environment (~13 GB)
mamba env create -f environments/diffdock_environment.yaml --prefix forks/DiffDock/DiffDock/
conda activate forks/DiffDock/DiffDock/ # NOTE: one still needs to use `conda` to (de)activate environments
# - FABind environment (~6 GB)
mamba env create -f environments/fabind_environment.yaml --prefix forks/FABind/FABind/
conda activate forks/FABind/FABind/ # NOTE: one still needs to use `conda` to (de)activate environments
# - DynamicBind environment (~13 GB)
mamba env create -f environments/dynamicbind_environment.yaml --prefix forks/DynamicBind/DynamicBind/
conda activate forks/DynamicBind/DynamicBind/ # NOTE: one still needs to use `conda` to (de)activate environments
# - NeuralPLexer environment (~14 GB)
mamba env create -f environments/neuralplexer_environment.yaml --prefix forks/NeuralPLexer/NeuralPLexer/
conda activate forks/NeuralPLexer/NeuralPLexer/ # NOTE: one still needs to use `conda` to (de)activate environments
cd forks/NeuralPLexer/ && pip3 install -e . && cd ../../
# - FlowDock environment (~14 GB)
mamba env create -f environments/flowdock_environment.yaml --prefix forks/FlowDock/FlowDock/
conda activate forks/FlowDock/FlowDock/ # NOTE: one still needs to use `conda` to (de)activate environments
cd forks/FlowDock/ && pip3 install -e . && cd ../../
# - RoseTTAFold-All-Atom environment (~14 GB) - NOTE: after running these commands, follow the installation instructions in `forks/RoseTTAFold-All-Atom/README.md` starting at Step 4 (with `forks/RoseTTAFold-All-Atom/` as the current working directory)
mamba env create -f environments/rfaa_environment.yaml --prefix forks/RoseTTAFold-All-Atom/RFAA/
conda activate forks/RoseTTAFold-All-Atom/RFAA/ # NOTE: one still needs to use `conda` to (de)activate environments
cd forks/RoseTTAFold-All-Atom/rf2aa/SE3Transformer/ && pip3 install --no-cache-dir -r requirements.txt && python3 setup.py install && cd ../../../../
# - Chai-1 environment (~6 GB)
mamba env create -f environments/chai_lab_environment.yaml --prefix forks/chai-lab/chai-lab/
conda activate forks/chai-lab/chai-lab/ # NOTE: one still needs to use `conda` to (de)activate environments
pip3 install forks/chai-lab/
# - AutoDock Vina Tools environment (~1 GB)
mamba env create -f environments/adfr_environment.yaml --prefix forks/Vina/ADFR/
conda activate forks/Vina/ADFR/ # NOTE: one still needs to use `conda` to (de)activate environments
# - P2Rank (~0.5 GB)
wget -P forks/P2Rank/ https://github.com/rdk/p2rank/releases/download/2.4.2/p2rank_2.4.2.tar.gz
tar -xzf forks/P2Rank/p2rank_2.4.2.tar.gz -C forks/P2Rank/
rm forks/P2Rank/p2rank_2.4.2.tar.gzDownload checkpoints (~8.25 GB total)
# DynamicBind checkpoint (~0.25 GB)
cd forks/DynamicBind/
wget https://zenodo.org/records/10137507/files/workdir.zip
unzip workdir.zip
rm workdir.zip
cd ../../
# NeuralPLexer checkpoint (~6.5 GB)
cd forks/NeuralPLexer/
wget https://zenodo.org/records/10373581/files/neuralplexermodels_downstream_datasets_predictions.zip
unzip neuralplexermodels_downstream_datasets_predictions.zip
rm neuralplexermodels_downstream_datasets_predictions.zip
cd ../../
# FlowDock checkpoint (~2 GB)
cd forks/FlowDock/
wget https://zenodo.org/records/14478459/files/flowdock_checkpoints.tar.gz
tar -xzf flowdock_checkpoints.tar.gz
rm flowdock_checkpoints.tar.gz
cd ../../
# RoseTTAFold-All-Atom checkpoint (~1.5 GB)
cd forks/RoseTTAFold-All-Atom/
wget http://files.ipd.uw.edu/pub/RF-All-Atom/weights/RFAA_paper_weights.pt
cd ../../We provide a two-part tutorial series of Jupyter notebooks to provide users with examples
of how to extend PoseBench, as outlined below.
# fetch, extract, and clean-up preprocessed Astex Diverse, PoseBusters Benchmark, DockGen, and CASP15 data (~3 GB) #
wget https://zenodo.org/records/14629652/files/astex_diverse_set.tar.gz
wget https://zenodo.org/records/14629652/files/posebusters_benchmark_set.tar.gz
wget https://zenodo.org/records/14629652/files/dockgen_set.tar.gz
wget https://zenodo.org/records/14629652/files/casp15_set.tar.gz
tar -xzf astex_diverse_set.tar.gz
tar -xzf posebusters_benchmark_set.tar.gz
tar -xzf dockgen_set.tar.gz
tar -xzf casp15_set.tar.gz
rm astex_diverse_set.tar.gz
rm posebusters_benchmark_set.tar.gz
rm dockgen_set.tar.gz
rm casp15_set.tar.gz# fetch, extract, and clean-up benchmark method predictions to reproduce paper results (~19 GB) #
# AutoDock Vina predictions and results
wget https://zenodo.org/records/14629652/files/vina_benchmark_method_predictions.tar.gz
tar -xzf vina_benchmark_method_predictions.tar.gz
rm vina_benchmark_method_predictions.tar.gz
# DiffDock predictions and results
wget https://zenodo.org/records/14629652/files/diffdock_benchmark_method_predictions.tar.gz
tar -xzf diffdock_benchmark_method_predictions.tar.gz
rm diffdock_benchmark_method_predictions.tar.gz
# DynamicBind predictions and results
wget https://zenodo.org/records/14629652/files/dynamicbind_benchmark_method_predictions.tar.gz
tar -xzf dynamicbind_benchmark_method_predictions.tar.gz
rm dynamicbind_benchmark_method_predictions.tar.gz
# NeuralPLexer predictions and results
wget https://zenodo.org/records/14629652/files/neuralplexer_benchmark_method_predictions.tar.gz
tar -xzf neuralplexer_benchmark_method_predictions.tar.gz
rm neuralplexer_benchmark_method_predictions.tar.gz
# RoseTTAFold-All-Atom predictions and results
wget https://zenodo.org/records/14629652/files/rfaa_benchmark_method_predictions.tar.gz
tar -xzf rfaa_benchmark_method_predictions.tar.gz
rm rfaa_benchmark_method_predictions.tar.gz
# Chai-1 predictions and results
wget https://zenodo.org/records/14629652/files/chai_benchmark_method_predictions.tar.gz
tar -xzf chai_benchmark_method_predictions.tar.gz
rm chai_benchmark_method_predictions.tar.gz
# AlphaFold 3 predictions and results
wget https://zenodo.org/records/14629652/files/af3_benchmark_method_predictions.tar.gz
tar -xzf af3_benchmark_method_predictions.tar.gz
rm af3_benchmark_method_predictions.tar.gz
# CASP15 predictions and results for all methods
wget https://zenodo.org/records/14629652/files/casp15_benchmark_method_predictions.tar.gz
tar -xzf casp15_benchmark_method_predictions.tar.gz
rm casp15_benchmark_method_predictions.tar.gz# fetch, extract, and clean-up benchmark method interactions to reproduce paper results (~12 GB) #
# cached ProLIF interactions for notebook plots
wget https://zenodo.org/records/14629652/files/posebench_notebooks.tar.gz
tar -xzf posebench_notebooks.tar.gz
rm posebench_notebooks.tar.gz# acquire multiple sequence alignment databases for RoseTTAFold-All-Atom (~2.5 TB)
cd forks/RoseTTAFold-All-Atom/
# uniref30 [46G]
wget http://wwwuser.gwdg.de/~compbiol/uniclust/2020_06/UniRef30_2020_06_hhsuite.tar.gz
mkdir -p UniRef30_2020_06
tar xfz UniRef30_2020_06_hhsuite.tar.gz -C ./UniRef30_2020_06
# BFD [272G]
wget https://bfd.mmseqs.com/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt.tar.gz
mkdir -p bfd
tar xfz bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt.tar.gz -C ./bfd
# structure templates [81G] (including *_a3m.ffdata, *_a3m.ffindex)
wget https://files.ipd.uw.edu/pub/RoseTTAFold/pdb100_2021Mar03.tar.gz
tar xfz pdb100_2021Mar03.tar.gz
cd ../../# download and extract the PDB's FASTA sequence files
mkdir -p ./data/pdb_data/
wget -P ./data/pdb_data/ https://files.rcsb.org/pub/pdb/derived_data/pdb_seqres.txt.gz
find ./data/pdb_data/ -type f -name "*.gz" -exec gzip -d {} \;First create all the corresponding FASTA files for each protein sequence
python3 posebench/data/components/fasta_preparation.py dataset=posebusters_benchmark
python3 posebench/data/components/fasta_preparation.py dataset=astex_diverse
python3 posebench/data/components/fasta_preparation.py dataset=dockgen
python3 posebench/data/components/fasta_preparation.py dataset=casp15To generate the apo version of each protein structure,
create ESMFold-ready versions of the combined FASTA files
prepared above by the script fasta_preparation.py
for the PoseBusters Benchmark and Astex Diverse sets, respectively
python3 posebench/data/components/esmfold_sequence_preparation.py dataset=posebusters_benchmark
python3 posebench/data/components/esmfold_sequence_preparation.py dataset=astex_diverse
python3 posebench/data/components/esmfold_sequence_preparation.py dataset=dockgen
python3 posebench/data/components/esmfold_sequence_preparation.py dataset=casp15Then, predict each apo protein structure using ESMFold's batch inference script
python3 posebench/data/components/esmfold_batch_structure_prediction.py -i data/posebusters_benchmark_set/reference_posebusters_benchmark_esmfold_sequences.fasta -o data/posebusters_benchmark_set/posebusters_benchmark_esmfold_predicted_structures --skip-existing
python3 posebench/data/components/esmfold_batch_structure_prediction.py -i data/astex_diverse_set/reference_astex_diverse_esmfold_sequences.fasta -o data/astex_diverse_set/astex_diverse_esmfold_predicted_structures --skip-existing
python3 posebench/data/components/esmfold_batch_structure_prediction.py -i data/dockgen_set/reference_dockgen_esmfold_sequences.fasta -o data/dockgen_set/dockgen_esmfold_predicted_structures --skip-existing
python3 posebench/data/components/esmfold_batch_structure_prediction.py -i data/casp15_set/reference_casp15_esmfold_sequences.fasta -o data/casp15_set/casp15_esmfold_predicted_structures --skip-existingNOTE: Having a CUDA-enabled device available when running ESMFold is highly recommended
NOTE: ESMFold may not be able to predict apo protein structures for a handful of exceedingly-long (e.g., >2000 token) input sequences
Lastly, align each apo protein structure to its corresponding holo protein structure counterpart for each dataset, taking ligand conformations into account during each alignment
conda activate PyMOL-PoseBench
python3 posebench/data/components/protein_apo_to_holo_alignment.py dataset=posebusters_benchmark processing_esmfold_structures=true num_workers=1
python3 posebench/data/components/protein_apo_to_holo_alignment.py dataset=astex_diverse processing_esmfold_structures=true num_workers=1
python3 posebench/data/components/protein_apo_to_holo_alignment.py dataset=dockgen processing_esmfold_structures=true num_workers=1
python3 posebench/data/components/protein_apo_to_holo_alignment.py dataset=casp15 processing_esmfold_structures=true num_workers=1
conda deactivateNOTE: The preprocessed Astex Diverse, PoseBusters Benchmark, DockGen, and CASP15 data available via Zenodo provide pre-holo-aligned protein structures predicted by AlphaFold 3 (and alternatively MIT-licensed ESMFold) for these respective datasets. Accordingly, users must ensure their usage of such predicted protein structures from AlphaFold 3 aligns with AlphaFold 3's Terms of Use.
| Name | Source | Astex Benchmarked | PoseBusters Benchmarked | DockGen Benchmarked | CASP Benchmarked |
|---|---|---|---|---|---|
DiffDock |
Corso et al. | ✓ | ✓ | ✓ | ✓ |
FABind |
Pei et al. | ✓ | ✓ | ✓ | ✗ |
AutoDock Vina |
Eberhardt et al. | ✓ | ✓ | ✓ | ✓ |
TULIP |
✓ | ✓ | ✗ | ✓ |
| Name | Source | Astex Benchmarked | PoseBusters Benchmarked | DockGen Benchmarked | CASP Benchmarked |
|---|---|---|---|---|---|
DynamicBind |
Lu et al. | ✓ | ✓ | ✓ | ✓ |
NeuralPLexer |
Qiao et al. | ✓ | ✓ | ✓ | ✓ |
FlowDock |
Morehead et al. | ✓ | ✓ | ✓ | ✓ |
RoseTTAFold-All-Atom |
Krishna et al. | ✓ | ✓ | ✓ | ✓ |
Chai-1 |
Chai Discovery | ✓ | ✓ | ✓ | ✓ |
AlphaFold 3 |
Abramson et al. | ✓ | ✓ | ✓ | ✓ |
| Name | Source | Astex Benchmarked | PoseBusters Benchmarked | DockGen Benchmarked | CASP Benchmarked |
|---|---|---|---|---|---|
DiffDock |
Corso et al. | ✓ | ✓ | ✓ | ✓ |
AutoDock Vina |
Eberhardt et al. | ✓ | ✓ | ✓ | ✓ |
TULIP |
✓ | ✓ | ✗ | ✓ |
| Name | Source | Astex Benchmarked | PoseBusters Benchmarked | DockGen Benchmarked | CASP Benchmarked |
|---|---|---|---|---|---|
DynamicBind |
Lu et al. | ✓ | ✓ | ✓ | ✓ |
NeuralPLexer |
Qiao et al. | ✓ | ✓ | ✓ | ✓ |
FlowDock |
Morehead et al. | ✓ | ✓ | ✓ | ✓ |
RoseTTAFold-All-Atom |
Krishna et al. | ✓ | ✓ | ✓ | ✓ |
Chai-1 |
Chai Discovery | ✓ | ✓ | ✓ | ✓ |
AlphaFold 3 |
Abramson et al. | ✓ | ✓ | ✓ | ✓ |
NOTE: Have a new method to add? Please let us know by creating a pull request. We would be happy to work with you to integrate new methodology into this benchmark!
Build inference scripts for one's desired sweep
python3 scripts/build_inference_script.py sweep=true export_hpc_headers=trueSubmit the inference scripts for job scheduling
sbatch scripts/inference/*_inference_*.shNOTE: See the config file configs/scripts/build_inference_script.yaml for more details.
Prepare CSV input files
python3 posebench/data/diffdock_input_preparation.py dataset=posebusters_benchmark
python3 posebench/data/diffdock_input_preparation.py dataset=astex_diverse
python3 posebench/data/diffdock_input_preparation.py dataset=dockgen
python3 posebench/data/diffdock_input_preparation.py dataset=casp15 input_data_dir=data/casp15_set/targets input_protein_structure_dir=data/casp15_set/casp15_holo_aligned_predicted_structuresRun inference on each dataset
python3 posebench/models/diffdock_inference.py dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/models/diffdock_inference.py dataset=astex_diverse repeat_index=1
...
python3 posebench/models/diffdock_inference.py dataset=dockgen repeat_index=1
...
python3 posebench/models/diffdock_inference.py dataset=casp15 batch_size=1 repeat_index=1
...Relax the generated ligand structures inside of their respective protein pockets
python3 posebench/models/inference_relaxation.py method=diffdock dataset=posebusters_benchmark remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=diffdock dataset=astex_diverse remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=diffdock dataset=dockgen remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...Analyze inference results for each dataset
python3 posebench/analysis/inference_analysis.py method=diffdock dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=diffdock dataset=astex_diverse repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=diffdock dataset=dockgen repeat_index=1
...Analyze inference results for the CASP15 dataset
# first assemble (unrelaxed and post ranking-relaxed) CASP15-compliant prediction submission files for scoring
python3 posebench/models/ensemble_generation.py ensemble_methods=\[diffdock\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_diffdock_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=false export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py ensemble_methods=\[diffdock\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_diffdock_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=true export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
# NOTE: the suffixes for both `output_dir` and `ensemble_benchmarking_repeat_index` should be modified to e.g., 2, 3, ...
...
# now score the CASP15-compliant submissions using the official CASP scoring pipeline
python3 posebench/analysis/inference_analysis_casp.py method=diffdock dataset=casp15 repeat_index=1
...Prepare CSV input files
python3 posebench/data/fabind_input_preparation.py dataset=posebusters_benchmark
python3 posebench/data/fabind_input_preparation.py dataset=astex_diverse
python3 posebench/data/fabind_input_preparation.py dataset=dockgenRun inference on each dataset
python3 posebench/models/fabind_inference.py dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/models/fabind_inference.py dataset=astex_diverse repeat_index=1
...
python3 posebench/models/fabind_inference.py dataset=dockgen repeat_index=1
...Relax the generated ligand structures inside of their respective protein pockets
python3 posebench/models/inference_relaxation.py method=fabind dataset=posebusters_benchmark remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=fabind dataset=astex_diverse remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=fabind dataset=dockgen remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...Analyze inference results for each dataset
python3 posebench/analysis/inference_analysis.py method=fabind dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=fabind dataset=astex_diverse repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=fabind dataset=dockgen repeat_index=1
...Prepare CSV input files
python3 posebench/data/dynamicbind_input_preparation.py dataset=posebusters_benchmark
python3 posebench/data/dynamicbind_input_preparation.py dataset=astex_diverse
python3 posebench/data/dynamicbind_input_preparation.py dataset=dockgen
python3 posebench/data/dynamicbind_input_preparation.py dataset=casp15 input_data_dir=data/casp15_set/targetsRun inference on each dataset
python3 posebench/models/dynamicbind_inference.py dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/models/dynamicbind_inference.py dataset=astex_diverse repeat_index=1
...
python3 posebench/models/dynamicbind_inference.py dataset=dockgen repeat_index=1
...
python3 posebench/models/dynamicbind_inference.py dataset=casp15 batch_size=1 input_data_dir=data/casp15_set/casp15_holo_aligned_predicted_structures repeat_index=1
...Relax the generated ligand structures inside of their respective protein pockets
python3 posebench/models/inference_relaxation.py method=dynamicbind dataset=posebusters_benchmark remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=dynamicbind dataset=astex_diverse remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=dynamicbind dataset=dockgen remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...Analyze inference results for each dataset
python3 posebench/analysis/inference_analysis.py method=dynamicbind dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=dynamicbind dataset=astex_diverse repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=dynamicbind dataset=dockgen repeat_index=1
...Analyze inference results for the CASP15 dataset
# first assemble (unrelaxed and post ranking-relaxed) CASP15-compliant prediction submission files for scoring
python3 posebench/models/ensemble_generation.py ensemble_methods=\[dynamicbind\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_dynamicbind_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=false export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py ensemble_methods=\[dynamicbind\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_dynamicbind_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=true export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
# NOTE: the suffixes for both `output_dir` and `ensemble_benchmarking_repeat_index` should be modified to e.g., 2, 3, ...
...
# now score the CASP15-compliant submissions using the official CASP scoring pipeline
python3 posebench/analysis/inference_analysis_casp.py method=dynamicbind dataset=casp15 repeat_index=1
...Prepare CSV input files
python3 posebench/data/neuralplexer_input_preparation.py dataset=posebusters_benchmark
python3 posebench/data/neuralplexer_input_preparation.py dataset=astex_diverse
python3 posebench/data/neuralplexer_input_preparation.py dataset=dockgen
python3 posebench/data/neuralplexer_input_preparation.py dataset=casp15 input_data_dir=data/casp15_set/targets input_receptor_structure_dir=data/casp15_set/casp15_holo_aligned_predicted_structuresRun inference on each dataset
python3 posebench/models/neuralplexer_inference.py dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/models/neuralplexer_inference.py dataset=astex_diverse repeat_index=1
...
python3 posebench/models/neuralplexer_inference.py dataset=dockgen repeat_index=1
...
python3 posebench/models/neuralplexer_inference.py dataset=casp15 chunk_size=5 repeat_index=1
...Relax the generated ligand structures inside of their respective protein pockets
python3 posebench/models/inference_relaxation.py method=neuralplexer dataset=posebusters_benchmark remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=neuralplexer dataset=astex_diverse remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=neuralplexer dataset=dockgen remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...Align predicted protein-ligand structures to ground-truth complex structures
conda activate PyMOL-PoseBench
python3 posebench/analysis/complex_alignment.py method=neuralplexer dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/analysis/complex_alignment.py method=neuralplexer dataset=astex_diverse repeat_index=1
...
python3 posebench/analysis/complex_alignment.py method=neuralplexer dataset=dockgen repeat_index=1
...
conda deactivateAnalyze inference results for each dataset
python3 posebench/analysis/inference_analysis.py method=neuralplexer dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=neuralplexer dataset=astex_diverse repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=neuralplexer dataset=dockgen repeat_index=1
...Analyze inference results for the CASP15 dataset
# first assemble (unrelaxed and post ranking-relaxed) CASP15-compliant prediction submission files for scoring
python3 posebench/models/ensemble_generation.py ensemble_methods=\[neuralplexer\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_neuralplexer_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=false export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py ensemble_methods=\[neuralplexer\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_neuralplexer_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=true export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
# NOTE: the suffixes for both `output_dir` and `ensemble_benchmarking_repeat_index` should be modified to e.g., 2, 3, ...
...
# now score the CASP15-compliant submissions using the official CASP scoring pipeline
python3 posebench/analysis/inference_analysis_casp.py method=neuralplexer dataset=casp15 repeat_index=1
...Prepare CSV input files
python3 posebench/data/flowdock_input_preparation.py dataset=posebusters_benchmark
python3 posebench/data/flowdock_input_preparation.py dataset=astex_diverse
python3 posebench/data/flowdock_input_preparation.py dataset=dockgen
python3 posebench/data/flowdock_input_preparation.py dataset=casp15 input_data_dir=data/casp15_set/targets input_receptor_structure_dir=data/casp15_set/casp15_holo_aligned_predicted_structuresRun inference on each dataset
python3 posebench/models/flowdock_inference.py dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/models/flowdock_inference.py dataset=astex_diverse repeat_index=1
...
python3 posebench/models/flowdock_inference.py dataset=dockgen repeat_index=1
...
python3 posebench/models/flowdock_inference.py dataset=casp15 chunk_size=5 repeat_index=1
...Relax the generated ligand structures inside of their respective protein pockets
python3 posebench/models/inference_relaxation.py method=flowdock dataset=posebusters_benchmark remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=flowdock dataset=astex_diverse remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=flowdock dataset=dockgen remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...Align predicted protein-ligand structures to ground-truth complex structures
conda activate PyMOL-PoseBench
python3 posebench/analysis/complex_alignment.py method=flowdock dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/analysis/complex_alignment.py method=flowdock dataset=astex_diverse repeat_index=1
...
python3 posebench/analysis/complex_alignment.py method=flowdock dataset=dockgen repeat_index=1
...
conda deactivateAnalyze inference results for each dataset
python3 posebench/analysis/inference_analysis.py method=flowdock dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=flowdock dataset=astex_diverse repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=flowdock dataset=dockgen repeat_index=1
...Analyze inference results for the CASP15 dataset
# first assemble (unrelaxed and post ranking-relaxed) CASP15-compliant prediction submission files for scoring
python3 posebench/models/ensemble_generation.py ensemble_methods=\[flowdock\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_flowdock_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=false export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py ensemble_methods=\[flowdock\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_flowdock_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=true export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
# NOTE: the suffixes for both `output_dir` and `ensemble_benchmarking_repeat_index` should be modified to e.g., 2, 3, ...
...
# now score the CASP15-compliant submissions using the official CASP scoring pipeline
python3 posebench/analysis/inference_analysis_casp.py method=flowdock dataset=casp15 repeat_index=1
...Prepare CSV input files
python3 posebench/data/rfaa_input_preparation.py dataset=posebusters_benchmark
python3 posebench/data/rfaa_input_preparation.py dataset=astex_diverse
python3 posebench/data/rfaa_input_preparation.py dataset=dockgen
python3 posebench/data/rfaa_input_preparation.py dataset=casp15 input_data_dir=data/casp15_set/targetsRun inference on each dataset
conda activate forks/RoseTTAFold-All-Atom/RFAA/
python3 posebench/models/rfaa_inference.py dataset=posebusters_benchmark run_inference_directly=true
python3 posebench/models/rfaa_inference.py dataset=astex_diverse run_inference_directly=true
python3 posebench/models/rfaa_inference.py dataset=dockgen run_inference_directly=true
python3 posebench/models/rfaa_inference.py dataset=casp15 run_inference_directly=true
conda deactivateExtract predictions into separate files for proteins and ligands
python3 posebench/data/rfaa_output_extraction.py dataset=posebusters_benchmark
python3 posebench/data/rfaa_output_extraction.py dataset=astex_diverse
python3 posebench/data/rfaa_output_extraction.py dataset=dockgen
python3 posebench/data/rfaa_output_extraction.py dataset=casp15Relax the generated ligand structures inside of their respective protein pockets
python3 posebench/models/inference_relaxation.py method=rfaa dataset=posebusters_benchmark remove_initial_protein_hydrogens=true
python3 posebench/models/inference_relaxation.py method=rfaa dataset=astex_diverse remove_initial_protein_hydrogens=true
python3 posebench/models/inference_relaxation.py method=rfaa dataset=dockgen remove_initial_protein_hydrogens=trueAlign predicted protein-ligand structures to ground-truth complex structures
conda activate PyMOL-PoseBench
python3 posebench/analysis/complex_alignment.py method=rfaa dataset=posebusters_benchmark
python3 posebench/analysis/complex_alignment.py method=rfaa dataset=astex_diverse
python3 posebench/analysis/complex_alignment.py method=rfaa dataset=dockgen
conda deactivateAnalyze inference results for each dataset
python3 posebench/analysis/inference_analysis.py method=rfaa dataset=posebusters_benchmark
python3 posebench/analysis/inference_analysis.py method=rfaa dataset=astex_diverse
python3 posebench/analysis/inference_analysis.py method=rfaa dataset=dockgenAnalyze inference results for the CASP15 dataset
# first assemble (unrelaxed and post ranking-relaxed) CASP15-compliant prediction submission files for scoring
python3 posebench/models/ensemble_generation.py ensemble_methods=\[rfaa\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_rfaa_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=false export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py ensemble_methods=\[rfaa\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_rfaa_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=true export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
# NOTE: the suffixes for both `output_dir` and `ensemble_benchmarking_repeat_index` should be modified to e.g., 2, 3, ...
...
# now score the CASP15-compliant submissions using the official CASP scoring pipeline
python3 posebench/analysis/inference_analysis_casp.py method=rfaa dataset=casp15 repeat_index=1
...Prepare CSV input files
python3 posebench/data/chai_input_preparation.py dataset=posebusters_benchmark
python3 posebench/data/chai_input_preparation.py dataset=astex_diverse
python3 posebench/data/chai_input_preparation.py dataset=dockgen
python3 posebench/data/chai_input_preparation.py dataset=casp15 input_data_dir=data/casp15_set/targetsRun inference on each dataset
conda activate forks/chai-lab/chai-lab/
python3 posebench/models/chai_inference.py dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/models/chai_inference.py dataset=astex_diverse repeat_index=1
...
python3 posebench/models/chai_inference.py dataset=dockgen repeat_index=1
...
python3 posebench/models/chai_inference.py dataset=casp15 repeat_index=1
...
conda deactivateExtract predictions into separate files for proteins and ligands
python3 posebench/data/chai_output_extraction.py dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/data/chai_output_extraction.py dataset=astex_diverse repeat_index=1
...
python3 posebench/data/chai_output_extraction.py dataset=dockgen repeat_index=1
...
python3 posebench/data/chai_output_extraction.py dataset=casp15 repeat_index=1
...Relax the generated ligand structures inside of their respective protein pockets
python3 posebench/models/inference_relaxation.py method=chai-lab dataset=posebusters_benchmark remove_initial_protein_hydrogens=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=chai-lab dataset=astex_diverse remove_initial_protein_hydrogens=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=chai-lab dataset=dockgen remove_initial_protein_hydrogens=true repeat_index=1
...Align predicted protein-ligand structures to ground-truth complex structures
conda activate PyMOL-PoseBench
python3 posebench/analysis/complex_alignment.py method=chai-lab dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/analysis/complex_alignment.py method=chai-lab dataset=astex_diverse repeat_index=1
...
python3 posebench/analysis/complex_alignment.py method=chai-lab dataset=dockgen repeat_index=1
conda deactivate
...Analyze inference results for each dataset
python3 posebench/analysis/inference_analysis.py method=chai-lab dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=chai-lab dataset=astex_diverse repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=chai-lab dataset=dockgen repeat_index=1
...Analyze inference results for the CASP15 dataset
# first assemble (unrelaxed and post ranking-relaxed) CASP15-compliant prediction submission files for scoring
python3 posebench/models/ensemble_generation.py ensemble_methods=\[chai-lab\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_chai-lab_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=false export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py ensemble_methods=\[chai-lab\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_chai-lab_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=true export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
# NOTE: the suffixes for both `output_dir` and `ensemble_benchmarking_repeat_index` should be modified to e.g., 2, 3, ...
...
# now score the CASP15-compliant submissions using the official CASP scoring pipeline
python3 posebench/analysis/inference_analysis_casp.py method=chai-lab dataset=casp15 repeat_index=1
...Run inference (3x) using the academically-available inference code released on GitHub, saving each run's structures to a unique output directory located at forks/alphafold3/prediction_outputs/{dataset=posebusters_benchmark,astex_diverse,dockgen,casp15}_{repeat_index=1,2,3}
Then, extract predictions into separate files for proteins and ligands
python3 posebench/data/af3_output_extraction.py dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/data/af3_output_extraction.py dataset=astex_diverse repeat_index=1
...
python3 posebench/data/af3_output_extraction.py dataset=dockgen repeat_index=1
...
python3 posebench/data/af3_output_extraction.py dataset=casp15 repeat_index=1
...Relax the generated ligand structures inside of their respective protein pockets
python3 posebench/models/inference_relaxation.py method=alphafold3 dataset=posebusters_benchmark remove_initial_protein_hydrogens=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=alphafold3 dataset=astex_diverse remove_initial_protein_hydrogens=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=alphafold3 dataset=dockgen remove_initial_protein_hydrogens=true repeat_index=1
...Align predicted protein-ligand structures to ground-truth complex structures
conda activate PyMOL-PoseBench
python3 posebench/analysis/complex_alignment.py method=alphafold3 dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/analysis/complex_alignment.py method=alphafold3 dataset=astex_diverse repeat_index=1
...
python3 posebench/analysis/complex_alignment.py method=alphafold3 dataset=dockgen repeat_index=1
conda deactivate
...Analyze inference results for each dataset
python3 posebench/analysis/inference_analysis.py method=alphafold3 dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=alphafold3 dataset=astex_diverse repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=alphafold3 dataset=dockgen repeat_index=1
...Analyze inference results for the CASP15 dataset
# first assemble (unrelaxed and post ranking-relaxed) CASP15-compliant prediction submission files for scoring
python3 posebench/models/ensemble_generation.py ensemble_methods=\[alphafold3\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_alphafold3_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=false export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py ensemble_methods=\[alphafold3\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_alphafold3_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=true export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
# NOTE: the suffixes for both `output_dir` and `ensemble_benchmarking_repeat_index` should be modified to e.g., 2, 3, ...
...
# now score the CASP15-compliant submissions using the official CASP scoring pipeline
python3 posebench/analysis/inference_analysis_casp.py method=alphafold3 dataset=casp15 repeat_index=1
...Prepare CSV input files
cp forks/DiffDock/inference/diffdock_posebusters_benchmark_inputs.csv forks/Vina/inference/vina_posebusters_benchmark_inputs.csv
cp forks/DiffDock/inference/diffdock_astex_diverse_inputs.csv forks/Vina/inference/vina_astex_diverse_inputs.csv
cp forks/DiffDock/inference/diffdock_dockgen_inputs.csv forks/Vina/inference/vina_dockgen_inputs.csv
cp forks/DiffDock/inference/diffdock_casp15_inputs.csv forks/Vina/inference/vina_casp15_inputs.csvRun inference on each dataset
python3 posebench/models/vina_inference.py dataset=posebusters_benchmark method=p2rank repeat_index=1 # NOTE: P2Rank's binding pockets are recommended as the default Vina input
...
python3 posebench/models/vina_inference.py dataset=astex_diverse method=p2rank repeat_index=1
...
python3 posebench/models/vina_inference.py dataset=dockgen method=p2rank repeat_index=1
...
python3 posebench/models/vina_inference.py dataset=casp15 method=p2rank repeat_index=1
...Copy Vina's predictions to the corresponding inference directory for each repeat
mkdir -p forks/Vina/inference/vina_p2rank_posebusters_benchmark_outputs_1 && cp -r data/test_cases/posebusters_benchmark/vina_p2rank_posebusters_benchmark_outputs_1/* forks/Vina/inference/vina_p2rank_posebusters_benchmark_outputs_1
...
mkdir -p forks/Vina/inference/vina_p2rank_astex_diverse_outputs_1 && cp -r data/test_cases/astex_diverse/vina_p2rank_astex_diverse_outputs_1/* forks/Vina/inference/vina_p2rank_astex_diverse_outputs_1
...
mkdir -p forks/Vina/inference/vina_p2rank_dockgen_outputs_1 && cp -r data/test_cases/dockgen/vina_p2rank_dockgen_outputs_1/* forks/Vina/inference/vina_p2rank_dockgen_outputs_1
...
mkdir -p forks/Vina/inference/vina_p2rank_casp15_outputs_1 && cp -r data/test_cases/casp15/vina_p2rank_casp15_outputs_1/* forks/Vina/inference/vina_p2rank_casp15_outputs_1
...Relax the generated ligand structures inside of their respective protein pockets
python3 posebench/models/inference_relaxation.py method=vina vina_binding_site_method=p2rank dataset=posebusters_benchmark remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=vina vina_binding_site_method=p2rank dataset=astex_diverse remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=vina vina_binding_site_method=p2rank dataset=dockgen remove_initial_protein_hydrogens=true assign_partial_charges_manually=true repeat_index=1
...Analyze inference results for each dataset
python3 posebench/analysis/inference_analysis.py method=vina vina_binding_site_method=p2rank dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=vina vina_binding_site_method=p2rank dataset=astex_diverse repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=vina vina_binding_site_method=p2rank dataset=dockgen repeat_index=1
...Analyze inference results for the CASP15 dataset
# assemble (unrelaxed and post ranking-relaxed) CASP15-compliant prediction submission files for scoring
python3 posebench/models/ensemble_generation.py ensemble_methods=\[vina\] vina_binding_site_methods=\[p2rank\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_vina_p2rank_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=false export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py ensemble_methods=\[vina\] vina_binding_site_methods=\[p2rank\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_vina_p2rank_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=true export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
# NOTE: the suffixes for both `output_dir` and `ensemble_benchmarking_repeat_index` should be modified to e.g., 2, 3, ...
...
# now score the CASP15-compliant submissions using the official CASP scoring pipeline
python3 posebench/analysis/inference_analysis_casp.py method=vina vina_binding_site_method=p2rank dataset=casp15 repeat_index=1
...Gather all template ligands generated by TULIP via its dedicated GitHub repository and collate the resulting ligand fragment SDF files
python3 posebench/data/tulip_output_extraction.py dataset=posebusters_benchmark
python3 posebench/data/tulip_output_extraction.py dataset=astex_diverse
python3 posebench/data/tulip_output_extraction.py dataset=dockgen
python3 posebench/data/tulip_output_extraction.py dataset=casp15Relax the generated ligand structures inside of their respective protein pockets
python3 posebench/models/inference_relaxation.py method=tulip dataset=posebusters_benchmark remove_initial_protein_hydrogens=true assign_partial_charges_manually=true
...
python3 posebench/models/inference_relaxation.py method=tulip dataset=astex_diverse remove_initial_protein_hydrogens=true assign_partial_charges_manually=true
...
python3 posebench/models/inference_relaxation.py method=tulip dataset=dockgen remove_initial_protein_hydrogens=true assign_partial_charges_manually=true
...Analyze inference results for each dataset
python3 posebench/analysis/inference_analysis.py method=tulip dataset=posebusters_benchmark
...
python3 posebench/analysis/inference_analysis.py method=tulip dataset=astex_diverse
...
python3 posebench/analysis/inference_analysis.py method=tulip dataset=dockgen
...Analyze inference results for the CASP15 dataset
# then assemble (unrelaxed and post ranking-relaxed) CASP15-compliant prediction submission files for scoring
python3 posebench/models/ensemble_generation.py ensemble_methods=\[tulip\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_tulip_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=false export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py ensemble_methods=\[tulip\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_tulip_ensemble_predictions_1 skip_existing=true relax_method_ligands_post_ranking=true export_file_format=casp15 export_top_n=5 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=5 resume=true ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 cuda_device_index=0 ensemble_benchmarking_repeat_index=1
# NOTE: the suffixes for both `output_dir` and `ensemble_benchmarking_repeat_index` should be modified to e.g., 2, 3, ...
...
# now score the CASP15-compliant submissions using the official CASP scoring pipeline
python3 posebench/analysis/inference_analysis_casp.py method=tulip dataset=casp15
...Using an ensemble of methods, generate predictions for a new protein target using each method and (e.g., consensus-)rank the pool of predictions
# generate each method's prediction script for a target
# NOTE: to predict input ESMFold protein structures when they are not already locally available in `data/ensemble_proteins/`, e.g., on a SLURM cluster first run e.g., `srun --partition=gpu --gres=gpu:A100:1 --mem=59G --time=01:00:00 --pty bash` to ensure a GPU is available for inference
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/5S8I_2LY/ensemble_inputs.csv output_dir=data/test_cases/5S8I_2LY/top_consensus_ensemble_predictions_1 max_method_predictions=5 method_top_n_to_select=3 ensemble_ranking_method=consensus resume=false ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]'
# ...
# now, manually run each desired method's generated prediction script, with the exception of AutoDock Vina which uses other methods' predictions
# ...
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/5S8I_2LY/ensemble_inputs.csv output_dir=data/test_cases/5S8I_2LY/top_consensus_ensemble_predictions_1 max_method_predictions=5 method_top_n_to_select=3 ensemble_ranking_method=consensus resume=true generate_vina_scripts=true vina_binding_site_methods=[p2rank]
# now, manually run AutoDock Vina's generated prediction script for each binding site prediction method
#...
# lastly, organize each method's predictions together
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/5S8I_2LY/ensemble_inputs.csv output_dir=data/test_cases/5S8I_2LY/top_consensus_ensemble_predictions_1 max_method_predictions=5 method_top_n_to_select=3 ensemble_ranking_method=consensus resume=true generate_vina_scripts=false vina_binding_site_methods=[p2rank]Benchmark (ensemble-)ranked predictions across each test dataset
# benchmark using the PoseBusters Benchmark dataset e.g., after generating 40 complexes per target with each method
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/posebusters_benchmark/ensemble_inputs.csv output_dir=data/test_cases/posebusters_benchmark/top_consensus_ensemble_predictions_1 max_method_predictions=5 method_top_n_to_select=3 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=false resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' ensemble_benchmarking=true ensemble_benchmarking_dataset=posebusters_benchmark ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/posebusters_benchmark/ensemble_inputs.csv output_dir=data/test_cases/posebusters_benchmark/top_consensus_ensemble_predictions_1 max_method_predictions=5 method_top_n_to_select=3 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=true resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' ensemble_benchmarking=true ensemble_benchmarking_dataset=posebusters_benchmark ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
...
# benchmark using the Astex Diverse dataset e.g., after generating 40 complexes per target with each method
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/astex_diverse/ensemble_inputs.csv output_dir=data/test_cases/astex_diverse/top_consensus_ensemble_predictions_1 max_method_predictions=5 method_top_n_to_select=3 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=false resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' ensemble_benchmarking=true ensemble_benchmarking_dataset=astex_diverse ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/astex_diverse/ensemble_inputs.csv output_dir=data/test_cases/astex_diverse/top_consensus_ensemble_predictions_1 max_method_predictions=5 method_top_n_to_select=3 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=true resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' ensemble_benchmarking=true ensemble_benchmarking_dataset=astex_diverse ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
...
# benchmark using the DockGen dataset e.g., after generating 40 complexes per target with each method
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/dockgen/ensemble_inputs.csv output_dir=data/test_cases/dockgen/top_consensus_ensemble_predictions_1 max_method_predictions=5 method_top_n_to_select=3 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=false resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' ensemble_benchmarking=true ensemble_benchmarking_dataset=dockgen ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/dockgen/ensemble_inputs.csv output_dir=data/test_cases/dockgen/top_consensus_ensemble_predictions_1 max_method_predictions=5 method_top_n_to_select=3 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=true resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' ensemble_benchmarking=true ensemble_benchmarking_dataset=dockgen ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
...
# benchmark using the CASP15 dataset e.g., after generating 40 complexes per target with each method
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_consensus_ensemble_predictions_1 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=3 export_top_n=5 export_file_format=casp15 skip_existing=true relax_method_ligands_post_ranking=false resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_consensus_ensemble_predictions_1 combine_casp_output_files=true max_method_predictions=5 method_top_n_to_select=3 export_top_n=5 export_file_format=casp15 skip_existing=true relax_method_ligands_post_ranking=true resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
...
# analyze benchmarking results for the PoseBusters Benchmark dataset
python3 posebench/analysis/inference_analysis.py method=ensemble dataset=posebusters_benchmark repeat_index=1
...
# analyze benchmarking results for the Astex Diverse dataset
python3 posebench/analysis/inference_analysis.py method=ensemble dataset=astex_diverse repeat_index=1
...
# analyze benchmarking results for the DockGen dataset
python3 posebench/analysis/inference_analysis.py method=ensemble dataset=dockgen repeat_index=1
...
# analyze benchmarking results for the CASP15 dataset
python3 posebench/analysis/inference_analysis_casp.py method=ensemble dataset=casp15 repeat_index=1
...To benchmark ensemble ranking using the above commands, you must have already run the corresponding *_inference.py script for each method described in the section How to run inference with individual methods (with the exception of FABind, which will not referenced during CASP15 benchmarking)
NOTE: In addition to having consensus as an available value for ensemble_ranking_method, one can also set ensemble_ranking_method=ff to have the method ensemble's top-ranked predictions selected using the criterion of "minimum (molecular dynamics) force field energy" (albeit while incurring a very large runtime complexity)
Pre-compute and analyze the protein-ligand interactions of each method
cd notebooks/
python3 astex_method_interaction_analysis_plotting.py
python3 dockgen_method_interaction_analysis_plotting.py
python3 posebusters_method_interaction_analysis_plotting.py
python3 casp15_method_interaction_analysis_plotting.py
cd ../Execute (and customize as desired) notebooks to prepare paper-ready result plots
jupyter notebook notebooks/astex_diverse_inference_results_plotting.ipynb
jupyter notebook notebooks/dockgen_inference_results_plotting.ipynb
jupyter notebook notebooks/posebusters_benchmark_inference_results_plotting.ipynb
jupyter notebook notebooks/casp15_inference_results_plotting.ipynbInspect the failure modes of each method
jupyter notebook notebooks/failure_modes_analysis_plotting.ipynbWe use mamba to manage the project's underlying dependencies. Notably, to update the dependencies listed in a particular environments/*_environment.yml file:
mamba env export > env.yaml # e.g., run this after installing new dependencies locally within a given `conda` environment
diff environments/posebench_environment.yaml env.yaml # note the differences and copy accepted changes back into e.g., `environments/posebench_environment.yaml`
rm env.yaml # clean up temporary environment fileWe use pre-commit to automatically format the project's code. To set up pre-commit (one time only) for automatic code linting and formatting upon each execution of git commit:
pre-commit installTo manually reformat all files in the project as desired:
pre-commit run -aWe sphinx to maintain the project's code documentation. To build a local version of the project's sphinx documentation web pages:
# assuming you are located in the `PoseBench` top-level directory
pip install -r docs/.docs.requirements # one-time only
rm -rf docs/build/ && sphinx-build docs/source/ docs/build/ # NOTE: errors can safely be ignoredPoseBench builds upon the source code and data from the following projects:
- AutoDock-Vina
- alphafold3
- casp15_ligand
- chai-lab
- DiffDock
- DynamicBind
- FABind
- FlowDock
- lightning-hydra-template
- NeuralPLexer
- ProteinWorkshop
- posebusters
- posebusters_em
- RoseTTAFold-All-Atom
- tulip
We thank all their contributors and maintainers!
If you use the code or benchmark method predictions associated with this repository or otherwise find this work useful, please cite:
@inproceedings{morehead2024posebench,
title={Deep Learning for Protein-Ligand Docking: Are We There Yet?},
author={Morehead, Alex and Giri, Nabin and Liu, Jian and Cheng, Jianlin},
booktitle={ICML AI4Science Workshop},
year={2024},
note={selected as a spotlight presentation},
}Lastly, thanks to Stable Diffusion for generating this quaint representation of what my brain looked like after assembling this codebase. 💣
