posebench
Comprehensive benchmarking of protein-ligand structure prediction methods. (ICML 2024 AI4Science)
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Comprehensive benchmarking of protein-ligand structure prediction methods. (ICML 2024 AI4Science)
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benchmark
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molecular-docking
structure-prediction
Created over 1 year ago
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README.md
# PoseBench
[](https://arxiv.org/abs/2405.14108)
[](https://doi.org/10.5281/zenodo.16791095)
[](https://badge.fury.io/py/posebench)
[](https://www.repostatus.org/#active)
[](https://bioinfomachinelearning.github.io/PoseBench/)
[](https://opensource.org/licenses/MIT)
[](https://huggingface.co/papers/2405.14108)
[](https://opensource.org/licenses/MIT)
[](https://huggingface.co/papers/2405.14108)
Description
Comprehensive benchmarking of protein-ligand structure prediction methods
Contents
- 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
Installation
### Portable installation
To reuse modules and utilities within `PoseBench` in other projects, one can simply use `pip`
```bash
pip install posebench
```
### Full installation
To 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)
```bash
wget "https://github.com/conda-forge/miniforge/releases/download/24.11.3-0/Miniforge3-$(uname)-$(uname -m).sh"
bash Miniforge3-$(uname)-$(uname -m).sh # accept all terms and install to the default location
rm Miniforge3-$(uname)-$(uname -m).sh # (optionally) remove installer after using it
source ~/.bashrc # alternatively, one can restart their shell session to achieve the same result
```
Install dependencies for each method's environment (as desired)
```bash
# 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
# NOTE: must comment out the `posecheck` requirement in `pyproject.toml` beforehand and restore it thereafter
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
pip3 install pyg-lib -f https://data.pyg.org/whl/torch-2.1.0+cu118.html
# - 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/ && pip3 install pyg-lib -f https://data.pyg.org/whl/torch-2.1.0+cu118.html # NOTE: one still needs to use
# - 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/
# - Boltz environment (~5 GB)
mamba env create -f environments/boltz_environment.yaml --prefix forks/boltz/boltz/
conda activate forks/boltz/boltz/ # NOTE: one still needs to use `conda` to (de)activate environments
cd forks/boltz/ && pip3 install -e .[cuda] && cd ../../
# - 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.gz
```
Download checkpoints (~8.25 GB total)
```bash
# 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 ../../
```
(Optional) Download PLINDER-based similarity metrics for method generalization analysis (~0.5 GB total)
```bash
mkdir -p ./data/plinder/
wget -P ./data/plinder/ https://zenodo.org/records/16754298/files/annotations.csv
wget -P ./data/plinder/ https://zenodo.org/records/16754298/files/all_similarity_scores.parquet
```
(Optional) Alternatively, download PLINDER to perform a method generalization analysis for custom (new) datasets (~500 GB total)
```bash
# download fixed version of PLINDER
export PLINDER_RELEASE=2024-06
export PLINDER_ITERATION=v2
mkdir -p ./data/plinder/${PLINDER_RELEASE}/${PLINDER_ITERATION}/
gsutil -m cp -r "gs://plinder/${PLINDER_RELEASE}/${PLINDER_ITERATION}/*" ./data/plinder/${PLINDER_RELEASE}/${PLINDER_ITERATION}/
# unpack system files of fixed version of PLINDER
cd ./data/plinder/${PLINDER_RELEASE}/${PLINDER_ITERATION}/systems; for i in `ls *zip`; do unzip $i; touch ${i//.zip/}_done; done
cd ../../../../../
# customize `similarity_scoring.py` to similarity-match a (bespoke) subset of new PDB complex IDs for (blind) benchmarking
python3 posebench/analysis/similarity_scoring.py $PDB_ID_FROM_NEW_SUBSET # e.g., in a for-loop or in parallel
# combine each score of the new subset into a singular (new) `all_similarity_scores.parquet` file
python3 -c "import os, pandas as pd; from glob import glob; files = glob(os.path.join('scoring', 'scores', 'scores', 'all_scores', '*.parquet')); pd.concat([pd.read_parquet(f) for f in files], ignore_index=True).to_parquet('data', 'plinder', 'all_similarity_scores.parquet')"
# update annotations
python3 -c "import re; pdb_ids_pattern = '|'.join(map(re.escape, $PDB_IDS_IN_NEW_SUBSET)); similarity_df_custom = all_similarity_scores[~all_similarity_scores["target_system"].str.contains(pdb_ids_pattern, na=False)].sort_values(by='sucos_shape_pocket_qcov', ascending=False).groupby('group_key').head(1).reset_index(drop=True); similarity_custom = dict(zip(similarity_df_custom['group_key'], similarity_df_custom['sucos_shape_pocket_qcov'])); annotated_df['sucos_shape_pocket_qcov_custom'] = annotated_df['group_key'].map(similarity_custom); annotated_df.to_csv(os.path.join('data', 'plinder', 'annotations.csv'))"
```
Tutorials
We provide a two-part tutorial series of Jupyter notebooks to provide users with examples
of how to extend `PoseBench`, as outlined below.
1. [Adding a new dataset](https://github.com/BioinfoMachineLearning/PoseBench/blob/main/notebooks/adding_new_dataset_tutorial.ipynb)
2. [Adding a new method](https://github.com/BioinfoMachineLearning/PoseBench/blob/main/notebooks/adding_new_method_tutorial.ipynb)
How to prepare PoseBench data
### Downloading Astex, PoseBusters, DockGen, and CASP15 data
```bash
# fetch, extract, and clean-up preprocessed Astex Diverse, PoseBusters Benchmark, DockGen, and CASP15 data (~3 GB) #
wget https://zenodo.org/records/16791095/files/astex_diverse_set.tar.gz
wget https://zenodo.org/records/16791095/files/posebusters_benchmark_set.tar.gz
wget https://zenodo.org/records/16791095/files/dockgen_set.tar.gz
wget https://zenodo.org/records/16791095/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
```
### Downloading benchmark method predictions
```bash
# fetch, extract, and clean-up benchmark method predictions to reproduce paper results (~19 GB) #
# AutoDock Vina predictions and results
wget https://zenodo.org/records/16791095/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/16791095/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/16791095/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/16791095/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/16791095/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/16791095/files/chai_benchmark_method_predictions.tar.gz
tar -xzf chai_benchmark_method_predictions.tar.gz
rm chai_benchmark_method_predictions.tar.gz
# Boltz-1 predictions and results
wget https://zenodo.org/records/16791095/files/boltz_benchmark_method_predictions.tar.gz
tar -xzf boltz_benchmark_method_predictions.tar.gz
rm boltz_benchmark_method_predictions.tar.gz
# AlphaFold 3 predictions and results
wget https://zenodo.org/records/16791095/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/16791095/files/casp15_benchmark_method_predictions.tar.gz
tar -xzf casp15_benchmark_method_predictions.tar.gz
rm casp15_benchmark_method_predictions.tar.gz
```
### Downloading benchmark method interactions
```bash
# 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/16791095/files/posebench_notebooks.tar.gz
tar -xzf posebench_notebooks.tar.gz
rm posebench_notebooks.tar.gz
```
### Downloading sequence databases (required only for RoseTTAFold-All-Atom inference)
```bash
# 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 ../../
```
### Downloading PDB metadata
```bash
# 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 {} \;
```
### Predicting apo protein structures using ESMFold (optional, preprocessed data available)
First create all the corresponding FASTA files for each protein sequence
```bash
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=casp15
```
To 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
```bash
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=casp15
```
Then, predict each apo protein structure using ESMFold's batch
inference script
```bash
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-existing
```
**NOTE:** 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
```bash
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 deactivate
```
**NOTE:** The preprocessed Astex Diverse, PoseBusters Benchmark, DockGen, and CASP15 data available via [Zenodo](https://doi.org/10.5281/zenodo.16791095) 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](https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md).
Available inference methods
### Methods available individually
#### Fixed Protein Methods
| Name | Source | Astex Benchmarked | PoseBusters Benchmarked | DockGen Benchmarked | CASP Benchmarked |
| --------------- | --------------------------------------------------------------------- | ----------------- | ----------------------- | ------------------- | ---------------- |
| `DiffDock` | [Corso et al.](https://openreview.net/forum?id=UfBIxpTK10) | | | | |
| `FABind` | [Pei et al.](https://openreview.net/forum?id=PnWakgg1RL) | | | | |
| `AutoDock Vina` | [Eberhardt et al.](https://pubs.acs.org/doi/10.1021/acs.jcim.1c00203) | | | | |
| `TULIP` | | | | | |
#### Flexible Protein Methods
| Name | Source | Astex Benchmarked | PoseBusters Benchmarked | DockGen Benchmarked | CASP Benchmarked |
| ---------------------- | ------------------------------------------------------------------------------ | ----------------- | ----------------------- | ------------------- | ---------------- |
| `DynamicBind` | [Lu et al.](https://www.nature.com/articles/s41467-024-45461-2) | | | | |
| `NeuralPLexer` | [Qiao et al.](https://www.nature.com/articles/s42256-024-00792-z) | | | | |
| `FlowDock` | [Morehead et al.](https://arxiv.org/abs/2412.10966) | | | | |
| `RoseTTAFold-All-Atom` | [Krishna et al.](https://www.science.org/doi/10.1126/science.adl2528) | | | | |
| `Chai-1` | [Chai Discovery](https://chaiassets.com/chai-1/paper/technical_report_v1.pdf) | | | | |
| `Boltz` | [Wohlwend et al.](https://www.biorxiv.org/content/10.1101/2024.11.19.624167v4) | | | | |
| `AlphaFold 3` | [Abramson et al.](https://www.nature.com/articles/s41586-024-07487-w) | | | | |
### Methods available for ensembling
#### Fixed Protein Methods
| Name | Source | Astex Benchmarked | PoseBusters Benchmarked | DockGen Benchmarked | CASP Benchmarked |
| --------------- | --------------------------------------------------------------------- | ----------------- | ----------------------- | ------------------- | ---------------- |
| `DiffDock` | [Corso et al.](https://openreview.net/forum?id=UfBIxpTK10) | | | | |
| `AutoDock Vina` | [Eberhardt et al.](https://pubs.acs.org/doi/10.1021/acs.jcim.1c00203) | | | | |
| `TULIP` | | | | | |
#### Flexible Protein Methods
| Name | Source | Astex Benchmarked | PoseBusters Benchmarked | DockGen Benchmarked | CASP Benchmarked |
| ---------------------- | ------------------------------------------------------------------------------ | ----------------- | ----------------------- | ------------------- | ---------------- |
| `DynamicBind` | [Lu et al.](https://www.nature.com/articles/s41467-024-45461-2) | | | | |
| `NeuralPLexer` | [Qiao et al.](https://www.nature.com/articles/s42256-024-00792-z) | | | | |
| `FlowDock` | [Morehead et al.](https://arxiv.org/abs/2412.10966) | | | | |
| `RoseTTAFold-All-Atom` | [Krishna et al.](https://www.science.org/doi/10.1126/science.adl2528) | | | | |
| `Chai-1` | [Chai Discovery](https://chaiassets.com/chai-1/paper/technical_report_v1.pdf) | | | | |
| `Boltz` | [Wohlwend et al.](https://www.biorxiv.org/content/10.1101/2024.11.19.624167v4) | | | | |
| `AlphaFold 3` | [Abramson et al.](https://www.nature.com/articles/s41586-024-07487-w) | | | | |
**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!
How to run a sweep of benchmarking experiments
Build inference scripts for one's desired sweep
```bash
python3 scripts/build_inference_script.py sweep=true export_hpc_headers=true
```
Submit the inference scripts for job scheduling
```bash
sbatch scripts/inference/*_inference_*.sh
```
**NOTE**: See the config file `configs/scripts/build_inference_script.yaml` for more details.
How to run inference with individual methods
### How to run inference with `DiffDock`
Prepare CSV input files
```bash
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_structures
```
Run inference on each dataset
```bash
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
```bash
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
```bash
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
```bash
# 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
...
```
### How to run inference with `FABind`
Prepare CSV input files
```bash
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=dockgen
```
Run inference on each dataset
```bash
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
```bash
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
```bash
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
...
```
### How to run inference with `DynamicBind`
Prepare CSV input files
```bash
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/targets
```
Run inference on each dataset
```bash
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
```bash
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
```bash
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
```bash
# 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
...
```
### How to run inference with `NeuralPLexer`
Prepare CSV input files
```bash
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_structures
```
Run inference on each dataset
```bash
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
```bash
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
```bash
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 deactivate
```
Analyze inference results for each dataset
```bash
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
```bash
# 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
...
```
### How to run inference with `FlowDock`
Prepare CSV input files
```bash
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_structures
```
Run inference on each dataset
```bash
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
```bash
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
```bash
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 deactivate
```
Analyze inference results for each dataset
```bash
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
```bash
# 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
...
```
### How to run inference with `RoseTTAFold-All-Atom`
Prepare CSV input files
```bash
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/targets
```
Run inference on each dataset
```bash
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 deactivate
```
Extract predictions into separate files for proteins and ligands
```bash
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=casp15
```
Relax the generated ligand structures inside of their respective protein pockets
```bash
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=true
```
Align predicted protein-ligand structures to ground-truth complex structures
```bash
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 deactivate
```
Analyze inference results for each dataset
```bash
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=dockgen
```
Analyze inference results for the CASP15 dataset
```bash
# 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
...
```
### How to run inference with `Chai-1`
Prepare CSV input files
```bash
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/targets
```
Run inference on each dataset
```bash
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 deactivate
```
Extract predictions into separate files for proteins and ligands
```bash
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
```bash
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
```bash
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
```bash
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
```bash
# 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
...
```
### How to run inference with `Boltz`
Prepare CSV input files
```bash
python3 posebench/data/boltz_input_preparation.py dataset=posebusters_benchmark
python3 posebench/data/boltz_input_preparation.py dataset=astex_diverse
python3 posebench/data/boltz_input_preparation.py dataset=dockgen
python3 posebench/data/boltz_input_preparation.py dataset=casp15 input_data_dir=data/casp15_set/targets
```
Run inference on each dataset
```bash
conda activate forks/boltz/boltz/
python3 posebench/models/boltz_inference.py dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/models/boltz_inference.py dataset=astex_diverse repeat_index=1
...
python3 posebench/models/boltz_inference.py dataset=dockgen repeat_index=1
...
python3 posebench/models/boltz_inference.py dataset=casp15 repeat_index=1
...
conda deactivate
```
Extract predictions into separate files for proteins and ligands
```bash
python3 posebench/data/boltz_output_extraction.py dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/data/boltz_output_extraction.py dataset=astex_diverse repeat_index=1
...
python3 posebench/data/boltz_output_extraction.py dataset=dockgen repeat_index=1
...
python3 posebench/data/boltz_output_extraction.py dataset=casp15 repeat_index=1
...
```
Relax the generated ligand structures inside of their respective protein pockets
```bash
python3 posebench/models/inference_relaxation.py method=boltz dataset=posebusters_benchmark remove_initial_protein_hydrogens=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=boltz dataset=astex_diverse remove_initial_protein_hydrogens=true repeat_index=1
...
python3 posebench/models/inference_relaxation.py method=boltz dataset=dockgen remove_initial_protein_hydrogens=true repeat_index=1
...
```
Align predicted protein-ligand structures to ground-truth complex structures
```bash
conda activate PyMOL-PoseBench
python3 posebench/analysis/complex_alignment.py method=boltz dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/analysis/complex_alignment.py method=boltz dataset=astex_diverse repeat_index=1
...
python3 posebench/analysis/complex_alignment.py method=boltz dataset=dockgen repeat_index=1
conda deactivate
...
```
Analyze inference results for each dataset
```bash
python3 posebench/analysis/inference_analysis.py method=boltz dataset=posebusters_benchmark repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=boltz dataset=astex_diverse repeat_index=1
...
python3 posebench/analysis/inference_analysis.py method=boltz dataset=dockgen repeat_index=1
...
```
Analyze inference results for the CASP15 dataset
```bash
# first assemble (unrelaxed and post ranking-relaxed) CASP15-compliant prediction submission files for scoring
python3 posebench/models/ensemble_generation.py ensemble_methods=\[boltz\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_boltz_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=\[boltz\] input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_boltz_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=boltz dataset=casp15 repeat_index=1
...
```
### How to run inference with `AlphaFold 3`
Run inference (3x) using the academically-available inference code released on [GitHub](https://github.com/google-deepmind/alphafold3), 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
```bash
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
```bash
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
```bash
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
```bash
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
```bash
# 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
...
```
### How to run inference with `AutoDock Vina`
Prepare CSV input files
```bash
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.csv
```
Run inference on each dataset
```bash
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
```bash
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
```bash
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
```bash
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
```bash
# 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
...
```
### How to run inference with `TULIP`
Gather all template ligands generated by `TULIP` via its dedicated [GitHub repository](https://github.com/BioinfoMachineLearning/tulip) and collate the resulting ligand fragment SDF files
```bash
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=casp15
```
Relax the generated ligand structures inside of their respective protein pockets
```bash
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
```bash
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
```bash
# 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
...
```
How to run inference with a method ensemble
Using an `ensemble` of methods, generate predictions for a new protein target using each method and (e.g., consensus-)rank the pool of predictions
```bash
# 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
```bash
# 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](#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)
How to create comparative plots of inference results
Pre-compute and analyze the protein-ligand interactions of each method
```bash
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
```bash
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.ipynb
```
Inspect the failure modes of each method
```bash
jupyter notebook notebooks/failure_modes_analysis_plotting_plinder.ipynb
# or
jupyter notebook notebooks/failure_modes_analysis_plotting.ipynb
```
For developers
### Dependency management
We use `mamba` to manage the project's underlying dependencies. Notably, to update the dependencies listed in a particular `environments/*_environment.yml` file:
```bash
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 file
```
### Code formatting
We 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`:
```bash
pre-commit install
```
To manually reformat all files in the project as desired:
```bash
pre-commit run -a
```
### Documentation
We `sphinx` to maintain the project's code documentation. To build a local version of the project's `sphinx` documentation web pages:
```bash
# 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 ignored
```
Acknowledgements
PoseBench builds upon the source code and data from the following projects:
- AutoDock-Vina
- alphafold3
- boltz
- casp15_ligand
- chai-lab
- DiffDock
- DynamicBind
- FABind
- FlowDock
- lightning-hydra-template
- NeuralPLexer
- plinder
- ProteinWorkshop
- posebusters
- posebusters_em
- RoseTTAFold-All-Atom
- runs-n-poses
- tulip
We thank all their contributors and maintainers!
Citing this work
If you use the code or benchmark method predictions associated with this repository or otherwise find this work useful, please cite:
bibtex
@inproceedings{morehead2024posebench,
title={Assessing the potential of deep learning for protein-ligand docking},
author={Morehead, Alex and Giri, Nabin and Liu, Jian and Neupane, Pawan and Cheng, Jianlin},
booktitle={ICML AI4Science Workshop},
year={2024},
note={selected as a spotlight presentation},
}
Bonus
Lastly, thanks to Stable Diffusion for generating this quaint representation of what my brain looked like after assembling this codebase.
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- Total issues: 2
- Total pull requests: 2
- Average time to close issues: 13 days
- Average time to close pull requests: 1 day
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- Total pull request authors: 2
- Average comments per issue: 2.0
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 1
Past Year
- Issues: 2
- Pull requests: 2
- Average time to close issues: 13 days
- Average time to close pull requests: 1 day
- Issue authors: 2
- Pull request authors: 2
- Average comments per issue: 2.0
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 1
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- chemgirl97 (2)
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- dependabot[bot] (3)
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Packages
- Total packages: 1
-
Total downloads:
- pypi 26 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 4
- Total maintainers: 1
pypi.org: posebench
Comprehensive benchmarking of protein-ligand structure prediction methods
- Homepage: https://pypi.org/project/posebench/
- Documentation: https://posebench.readthedocs.io/
- License: MIT License Copyright (c) 2024 BioinfoMachineLearning Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-
Latest release: 0.7.0
published 5 months ago
Rankings
Dependent packages count: 10.5%
Average: 34.8%
Dependent repos count: 59.1%
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Last synced:
5 months ago
Dependencies
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forks/DiffDock/Dockerfile
docker
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forks/DynamicBind/environment.yml
pypi
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