Olamide Isreal
a3ffec6a07
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472 lines
22 KiB
Markdown
472 lines
22 KiB
Markdown
<div align="center">
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# FlowDock
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<a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a>
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<a href="https://pytorchlightning.ai/"><img alt="Lightning" src="https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning&logoColor=white"></a>
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<a href="https://hydra.cc/"><img alt="Config: Hydra" src="https://img.shields.io/badge/Config-Hydra-89b8cd"></a>
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<!-- <a href="https://github.com/ashleve/lightning-hydra-template"><img alt="Template" src="https://img.shields.io/badge/-Lightning--Hydra--Template-017F2F?style=flat&logo=github&labelColor=gray"></a><br> -->
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[](https://arxiv.org/abs/2412.10966)
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[](https://academic.oup.com/bioinformatics/article/41/Supplement_1/i198/8199366)
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[](https://doi.org/10.5281/zenodo.15066450)
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<img src="./img/FlowDock.png" width="600">
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</div>
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## Description
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This is the official codebase of the paper
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**FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction**
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\[[arXiv](https://arxiv.org/abs/2412.10966)\] \[[ISMB](https://academic.oup.com/bioinformatics/article/41/Supplement_1/i198/8199366)\] \[[Neurosnap](https://neurosnap.ai/service/FlowDock)\] \[[Tamarind Bio](https://app.tamarind.bio/tools/flowdock)\]
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<div align="center">
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</div>
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## Contents
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- [FlowDock](#flowdock)
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- [Description](#description)
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- [Contents](#contents)
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- [Installation](#installation)
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- [How to prepare data for `FlowDock`](#how-to-prepare-data-for-flowdock)
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- [Generating ESM2 embeddings for each protein (optional, cached input data available on SharePoint)](#generating-esm2-embeddings-for-each-protein-optional-cached-input-data-available-on-sharepoint)
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- [Predicting apo protein structures using ESMFold (optional, cached data available on Zenodo)](#predicting-apo-protein-structures-using-esmfold-optional-cached-data-available-on-zenodo)
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- [How to train `FlowDock`](#how-to-train-flowdock)
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- [How to evaluate `FlowDock`](#how-to-evaluate-flowdock)
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- [How to create comparative plots of benchmarking results](#how-to-create-comparative-plots-of-benchmarking-results)
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- [How to predict new protein-ligand complex structures and their affinities using `FlowDock`](#how-to-predict-new-protein-ligand-complex-structures-and-their-affinities-using-flowdock)
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- [For developers](#for-developers)
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- [Docker](#docker)
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- [Acknowledgements](#acknowledgements)
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- [License](#license)
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- [Citing this work](#citing-this-work)
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## Installation
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<details>
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Install Mamba
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```bash
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wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
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bash Miniforge3-$(uname)-$(uname -m).sh # accept all terms and install to the default location
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rm Miniforge3-$(uname)-$(uname -m).sh # (optionally) remove installer after using it
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source ~/.bashrc # alternatively, one can restart their shell session to achieve the same result
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```
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Install dependencies
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```bash
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# clone project
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git clone https://github.com/BioinfoMachineLearning/FlowDock
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cd FlowDock
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# create conda environment
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mamba env create -f environments/flowdock_environment.yaml
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conda activate FlowDock # NOTE: one still needs to use `conda` to (de)activate environments
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pip3 install -e . # install local project as package
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pip3 install prody==2.4.1 --no-dependencies # install ProDy without NumPy dependency
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```
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Download checkpoints
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```bash
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# pretrained NeuralPLexer weights
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cd checkpoints/
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wget https://zenodo.org/records/10373581/files/neuralplexermodels_downstream_datasets_predictions.zip
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unzip neuralplexermodels_downstream_datasets_predictions.zip
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rm neuralplexermodels_downstream_datasets_predictions.zip
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cd ../
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```
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```bash
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# pretrained FlowDock weights
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wget https://zenodo.org/records/15066450/files/flowdock_checkpoints.tar.gz
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tar -xzf flowdock_checkpoints.tar.gz
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rm flowdock_checkpoints.tar.gz
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```
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Download preprocessed datasets
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```bash
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# cached input data for training/validation/testing
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wget "https://mailmissouri-my.sharepoint.com/:u:/g/personal/acmwhb_umsystem_edu/ER1hctIBhDVFjM7YepOI6WcBXNBm4_e6EBjFEHAM1A3y5g?download=1"
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tar -xzf flowdock_data_cache.tar.gz
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rm flowdock_data_cache.tar.gz
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# cached data for PDBBind, Binding MOAD, DockGen, and the PDB-based van der Mers (vdM) dataset
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wget https://zenodo.org/records/15066450/files/flowdock_pdbbind_data.tar.gz
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tar -xzf flowdock_pdbbind_data.tar.gz
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rm flowdock_pdbbind_data.tar.gz
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wget https://zenodo.org/records/15066450/files/flowdock_moad_data.tar.gz
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tar -xzf flowdock_moad_data.tar.gz
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rm flowdock_moad_data.tar.gz
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wget https://zenodo.org/records/15066450/files/flowdock_dockgen_data.tar.gz
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tar -xzf flowdock_dockgen_data.tar.gz
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rm flowdock_dockgen_data.tar.gz
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wget https://zenodo.org/records/15066450/files/flowdock_pdbsidechain_data.tar.gz
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tar -xzf flowdock_pdbsidechain_data.tar.gz
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rm flowdock_pdbsidechain_data.tar.gz
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```
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</details>
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## How to prepare data for `FlowDock`
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<details>
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**NOTE:** The following steps (besides downloading PDBBind and Binding MOAD's PDB files) are only necessary if one wants to fully process each of the following datasets manually.
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Otherwise, preprocessed versions of each dataset can be found on [Zenodo](https://zenodo.org/records/15066450).
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Download data
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```bash
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# fetch preprocessed PDBBind and Binding MOAD (as well as the optional DockGen and vdM datasets)
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cd data/
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wget "https://mailmissouri-my.sharepoint.com/:u:/g/personal/acmwhb_umsystem_edu/EXesf4oh6ztOusGqFcDyqP0Bvk-LdJ1DagEl8GNK-HxDtg?download=1"
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wget https://zenodo.org/records/10656052/files/BindingMOAD_2020_processed.tar
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wget https://zenodo.org/records/10656052/files/DockGen.tar
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wget https://files.ipd.uw.edu/pub/training_sets/pdb_2021aug02.tar.gz
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mv EXesf4oh6ztOusGqFcDyqP0Bvk-LdJ1DagEl8GNK-HxDtg?download=1 PDBBind.tar.gz
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tar -xzf PDBBind.tar.gz
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tar -xf BindingMOAD_2020_processed.tar
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tar -xf DockGen.tar
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tar -xzf pdb_2021aug02.tar.gz
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rm PDBBind.tar.gz BindingMOAD_2020_processed.tar DockGen.tar pdb_2021aug02.tar.gz
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mkdir pdbbind/ moad/ pdbsidechain/
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mv PDBBind_processed/ pdbbind/
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mv BindingMOAD_2020_processed/ moad/
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mv pdb_2021aug02/ pdbsidechain/
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cd ../
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```
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Lastly, to finetune `FlowDock` using the `PLINDER` dataset, one must first prepare this data for training
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```bash
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# fetch PLINDER data (NOTE: requires ~1 hour to download and ~750G of storage)
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export PLINDER_MOUNT="$(pwd)/data/PLINDER"
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mkdir -p "$PLINDER_MOUNT" # create the directory if it doesn't exist
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plinder_download -y
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```
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### Generating ESM2 embeddings for each protein (optional, cached input data available on SharePoint)
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To generate the ESM2 embeddings for the protein inputs,
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first create all the corresponding FASTA files for each protein sequence
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```bash
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python flowdock/data/components/esm_embedding_preparation.py --dataset pdbbind --data_dir data/pdbbind/PDBBind_processed/ --out_file data/pdbbind/pdbbind_sequences.fasta
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python flowdock/data/components/esm_embedding_preparation.py --dataset moad --data_dir data/moad/BindingMOAD_2020_processed/pdb_protein/ --out_file data/moad/moad_sequences.fasta
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python flowdock/data/components/esm_embedding_preparation.py --dataset dockgen --data_dir data/DockGen/processed_files/ --out_file data/DockGen/dockgen_sequences.fasta
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python flowdock/data/components/esm_embedding_preparation.py --dataset pdbsidechain --data_dir data/pdbsidechain/pdb_2021aug02/pdb/ --out_file data/pdbsidechain/pdbsidechain_sequences.fasta
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```
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Then, generate all ESM2 embeddings in batch using the ESM repository's helper script
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```bash
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python flowdock/data/components/esm_embedding_extraction.py esm2_t33_650M_UR50D data/pdbbind/pdbbind_sequences.fasta data/pdbbind/embeddings_output --repr_layers 33 --include per_tok --truncation_seq_length 4096 --cuda_device_index 0
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python flowdock/data/components/esm_embedding_extraction.py esm2_t33_650M_UR50D data/moad/moad_sequences.fasta data/moad/embeddings_output --repr_layers 33 --include per_tok --truncation_seq_length 4096 --cuda_device_index 0
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python flowdock/data/components/esm_embedding_extraction.py esm2_t33_650M_UR50D data/DockGen/dockgen_sequences.fasta data/DockGen/embeddings_output --repr_layers 33 --include per_tok --truncation_seq_length 4096 --cuda_device_index 0
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python flowdock/data/components/esm_embedding_extraction.py esm2_t33_650M_UR50D data/pdbsidechain/pdbsidechain_sequences.fasta data/pdbsidechain/embeddings_output --repr_layers 33 --include per_tok --truncation_seq_length 4096 --cuda_device_index 0
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```
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### Predicting apo protein structures using ESMFold (optional, cached data available on Zenodo)
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To generate the apo version of each protein structure,
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first create ESMFold-ready versions of the combined FASTA files
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prepared above by the script `esm_embedding_preparation.py`
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for the PDBBind, Binding MOAD, DockGen, and PDBSidechain datasets, respectively
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```bash
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python flowdock/data/components/esmfold_sequence_preparation.py dataset=pdbbind
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python flowdock/data/components/esmfold_sequence_preparation.py dataset=moad
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python flowdock/data/components/esmfold_sequence_preparation.py dataset=dockgen
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python flowdock/data/components/esmfold_sequence_preparation.py dataset=pdbsidechain
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```
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Then, predict each apo protein structure using ESMFold's batch
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inference script
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```bash
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# Note: Having a CUDA-enabled device available when running this script is highly recommended
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python flowdock/data/components/esmfold_batch_structure_prediction.py -i data/pdbbind/pdbbind_esmfold_sequences.fasta -o data/pdbbind/pdbbind_esmfold_structures --cuda-device-index 0 --skip-existing
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python flowdock/data/components/esmfold_batch_structure_prediction.py -i data/moad/moad_esmfold_sequences.fasta -o data/moad/moad_esmfold_structures --cuda-device-index 0 --skip-existing
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python flowdock/data/components/esmfold_batch_structure_prediction.py -i data/DockGen/dockgen_esmfold_sequences.fasta -o data/DockGen/dockgen_esmfold_structures --cuda-device-index 0 --skip-existing
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python flowdock/data/components/esmfold_batch_structure_prediction.py -i data/pdbsidechain/pdbsidechain_esmfold_sequences.fasta -o data/pdbsidechain/pdbsidechain_esmfold_structures --cuda-device-index 0 --skip-existing
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```
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Align each apo protein structure to its corresponding
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holo protein structure counterpart in PDBBind, Binding MOAD, and PDBSidechain,
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taking ligand conformations into account during each alignment
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```bash
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python flowdock/data/components/esmfold_apo_to_holo_alignment.py dataset=pdbbind num_workers=1
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python flowdock/data/components/esmfold_apo_to_holo_alignment.py dataset=moad num_workers=1
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python flowdock/data/components/esmfold_apo_to_holo_alignment.py dataset=dockgen num_workers=1
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python flowdock/data/components/esmfold_apo_to_holo_alignment.py dataset=pdbsidechain num_workers=1
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```
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Lastly, assess the apo-to-holo alignments in terms of statistics and structural metrics
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to enable runtime-dynamic dataset filtering using such information
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```bash
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python flowdock/data/components/esmfold_apo_to_holo_assessment.py dataset=pdbbind usalign_exec_path=$MY_USALIGN_EXEC_PATH
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python flowdock/data/components/esmfold_apo_to_holo_assessment.py dataset=moad usalign_exec_path=$MY_USALIGN_EXEC_PATH
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python flowdock/data/components/esmfold_apo_to_holo_assessment.py dataset=dockgen usalign_exec_path=$MY_USALIGN_EXEC_PATH
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python flowdock/data/components/esmfold_apo_to_holo_assessment.py dataset=pdbsidechain usalign_exec_path=$MY_USALIGN_EXEC_PATH
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```
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</details>
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## How to train `FlowDock`
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<details>
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Train model with default configuration
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```bash
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# train on CPU
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python flowdock/train.py trainer=cpu
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# train on GPU
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python flowdock/train.py trainer=gpu
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```
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Train model with chosen experiment configuration from [configs/experiment/](configs/experiment/)
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```bash
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python flowdock/train.py experiment=experiment_name.yaml
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```
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For example, reproduce `FlowDock`'s default model training run
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```bash
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python flowdock/train.py experiment=flowdock_fm
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```
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**Note:** You can override any parameter from command line like this
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```bash
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python flowdock/train.py experiment=flowdock_fm trainer.max_epochs=20 data.batch_size=8
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```
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For example, override parameters to finetune `FlowDock`'s pretrained weights using a new dataset such as [PLINDER](https://www.plinder.sh/)
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```bash
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python flowdock/train.py experiment=flowdock_fm data=plinder ckpt_path=checkpoints/esmfold_prior_paper_weights.ckpt
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```
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</details>
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## How to evaluate `FlowDock`
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<details>
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To reproduce `FlowDock`'s evaluation results for structure prediction, please refer to its documentation in version `0.6.0-FlowDock` of the [PoseBench](https://github.com/BioinfoMachineLearning/PoseBench/tree/0.6.0-FlowDock?tab=readme-ov-file#how-to-run-inference-with-flowdock) GitHub repository.
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To reproduce `FlowDock`'s evaluation results for binding affinity prediction using the PDBBind dataset
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```bash
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python flowdock/eval.py data.test_datasets=[pdbbind] ckpt_path=checkpoints/esmfold_prior_paper_weights-EMA.ckpt trainer=gpu
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... # re-run two more times to gather triplicate results
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```
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</details>
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## How to create comparative plots of benchmarking results
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<details>
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Download baseline method predictions and results
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```bash
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# cached predictions and evaluation metrics for reproducing structure prediction paper results
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wget https://zenodo.org/records/15066450/files/alphafold3_baseline_method_predictions.tar.gz
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tar -xzf alphafold3_baseline_method_predictions.tar.gz
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rm alphafold3_baseline_method_predictions.tar.gz
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wget https://zenodo.org/records/15066450/files/chai_baseline_method_predictions.tar.gz
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tar -xzf chai_baseline_method_predictions.tar.gz
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rm chai_baseline_method_predictions.tar.gz
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wget https://zenodo.org/records/15066450/files/diffdock_baseline_method_predictions.tar.gz
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tar -xzf diffdock_baseline_method_predictions.tar.gz
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rm diffdock_baseline_method_predictions.tar.gz
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wget https://zenodo.org/records/15066450/files/dynamicbind_baseline_method_predictions.tar.gz
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tar -xzf dynamicbind_baseline_method_predictions.tar.gz
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rm dynamicbind_baseline_method_predictions.tar.gz
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wget https://zenodo.org/records/15066450/files/flowdock_baseline_method_predictions.tar.gz
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tar -xzf flowdock_baseline_method_predictions.tar.gz
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rm flowdock_baseline_method_predictions.tar.gz
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wget https://zenodo.org/records/15066450/files/flowdock_aft_baseline_method_predictions.tar.gz
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tar -xzf flowdock_aft_baseline_method_predictions.tar.gz
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rm flowdock_aft_baseline_method_predictions.tar.gz
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wget https://zenodo.org/records/15066450/files/flowdock_pft_baseline_method_predictions.tar.gz
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tar -xzf flowdock_pft_baseline_method_predictions.tar.gz
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rm flowdock_pft_baseline_method_predictions.tar.gz
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wget https://zenodo.org/records/15066450/files/flowdock_esmfold_baseline_method_predictions.tar.gz
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tar -xzf flowdock_esmfold_baseline_method_predictions.tar.gz
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rm flowdock_esmfold_baseline_method_predictions.tar.gz
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wget https://zenodo.org/records/15066450/files/flowdock_chai_baseline_method_predictions.tar.gz
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tar -xzf flowdock_chai_baseline_method_predictions.tar.gz
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rm flowdock_chai_baseline_method_predictions.tar.gz
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wget https://zenodo.org/records/15066450/files/flowdock_hp_baseline_method_predictions.tar.gz
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tar -xzf flowdock_hp_baseline_method_predictions.tar.gz
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rm flowdock_hp_baseline_method_predictions.tar.gz
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wget https://zenodo.org/records/15066450/files/neuralplexer_baseline_method_predictions.tar.gz
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tar -xzf neuralplexer_baseline_method_predictions.tar.gz
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rm neuralplexer_baseline_method_predictions.tar.gz
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wget https://zenodo.org/records/15066450/files/vina_p2rank_baseline_method_predictions.tar.gz
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tar -xzf vina_p2rank_baseline_method_predictions.tar.gz
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rm vina_p2rank_baseline_method_predictions.tar.gz
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wget https://zenodo.org/records/15066450/files/rfaa_baseline_method_predictions.tar.gz
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tar -xzf rfaa_baseline_method_predictions.tar.gz
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rm rfaa_baseline_method_predictions.tar.gz
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```
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Reproduce paper result figures
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```bash
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jupyter notebook notebooks/casp16_binding_affinity_prediction_results_plotting.ipynb
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jupyter notebook notebooks/casp16_flowdock_vs_multicom_ligand_structure_prediction_results_plotting.ipynb
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jupyter notebook notebooks/dockgen_structure_prediction_results_plotting.ipynb
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jupyter notebook notebooks/posebusters_benchmark_structure_prediction_chemical_similarity_analysis.ipynb
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jupyter notebook notebooks/posebusters_benchmark_structure_prediction_results_plotting.ipynb
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```
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</details>
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## How to predict new protein-ligand complex structures and their affinities using `FlowDock`
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<details>
|
|
|
|
For example, generate new protein-ligand complexes for a pair of protein sequence and ligand SMILES strings such as those of the PDBBind 2020 test target `6i67`
|
|
|
|
```bash
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|
python flowdock/sample.py ckpt_path=checkpoints/esmfold_prior_paper_weights-EMA.ckpt model.cfg.prior_type=esmfold sampling_task=batched_structure_sampling input_receptor='YNKIVHLLVAEPEKIYAMPDPTVPDSDIKALTTLCDLADRELVVIIGWAKHIPGFSTLSLADQMSLLQSAWMEILILGVVYRSLFEDELVYADDYIMDEDQSKLAGLLDLNNAILQLVKKYKSMKLEKEEFVTLKAIALANSDSMHIEDVEAVQKLQDVLHEALQDYEAGQHMEDPRRAGKMLMTLPLLRQTSTKAVQHFYNKLEGKVPMHKLFLEMLEAKV' input_ligand='"c1cc2c(cc1O)CCCC2"' input_template=data/pdbbind/pdbbind_holo_aligned_esmfold_structures/6i67_holo_aligned_esmfold_protein.pdb sample_id='6i67' out_path='./6i67_sampled_structures/' n_samples=5 chunk_size=5 num_steps=40 sampler=VDODE sampler_eta=1.0 start_time='1.0' use_template=true separate_pdb=true visualize_sample_trajectories=true auxiliary_estimation_only=false esmfold_chunk_size=null trainer=gpu
|
|
```
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|
|
|
Or, for example, generate new protein-ligand complexes for pairs of protein sequences and (multi-)ligand SMILES strings (delimited via `|`) such as those of the CASP15 target `T1152`
|
|
|
|
```bash
|
|
python flowdock/sample.py ckpt_path=checkpoints/esmfold_prior_paper_weights-EMA.ckpt model.cfg.prior_type=esmfold sampling_task=batched_structure_sampling input_receptor='MYTVKPGDTMWKIAVKYQIGISEIIAANPQIKNPNLIYPGQKINIP|MYTVKPGDTMWKIAVKYQIGISEIIAANPQIKNPNLIYPGQKINIP|MYTVKPGDTMWKIAVKYQIGISEIIAANPQIKNPNLIYPGQKINIPN' input_ligand='"CC(=O)NC1C(O)OC(CO)C(OC2OC(CO)C(OC3OC(CO)C(O)C(O)C3NC(C)=O)C(O)C2NC(C)=O)C1O"' input_template=data/test_cases/predicted_structures/T1152.pdb sample_id='T1152' out_path='./T1152_sampled_structures/' n_samples=5 chunk_size=5 num_steps=40 sampler=VDODE sampler_eta=1.0 start_time='1.0' use_template=true separate_pdb=true visualize_sample_trajectories=true auxiliary_estimation_only=false esmfold_chunk_size=null trainer=gpu
|
|
```
|
|
|
|
If you do not already have a template protein structure available for your target of interest, set `input_template=null` to instead have the sampling script predict the ESMFold structure of your provided `input_protein` sequence before running the sampling pipeline. For more information regarding the input arguments available for sampling, please refer to the config at `configs/sample.yaml`.
|
|
|
|
**NOTE:** To optimize prediction runtimes, a `csv_path` can be specified instead of the `input_receptor`, `input_ligand`, and `input_template` CLI arguments to perform *batched* prediction for a collection of protein-ligand sequence pairs, each represented as a CSV row containing column values for `id`, `input_receptor`, `input_ligand`, and `input_template`. Additionally, disabling `visualize_sample_trajectories` may reduce storage requirements when predicting a large batch of inputs.
|
|
|
|
For instance, one can perform batched prediction as follows:
|
|
|
|
```bash
|
|
python flowdock/sample.py ckpt_path=checkpoints/esmfold_prior_paper_weights-EMA.ckpt model.cfg.prior_type=esmfold sampling_task=batched_structure_sampling csv_path='./data/test_cases/prediction_inputs/flowdock_batched_inputs.csv' out_path='./T1152_batch_sampled_structures/' n_samples=5 chunk_size=5 num_steps=40 sampler=VDODE sampler_eta=1.0 start_time='1.0' use_template=true separate_pdb=true visualize_sample_trajectories=false auxiliary_estimation_only=false esmfold_chunk_size=null trainer=gpu
|
|
```
|
|
|
|
</details>
|
|
|
|
## For developers
|
|
|
|
<details>
|
|
|
|
Set up `pre-commit` (one time only) for automatic code linting and formatting upon each `git commit`
|
|
|
|
```bash
|
|
pre-commit install
|
|
```
|
|
|
|
Manually reformat all files in the project, as desired
|
|
|
|
```bash
|
|
pre-commit run -a
|
|
```
|
|
|
|
Update dependencies in a `*_environment.yml` file
|
|
|
|
```bash
|
|
mamba env export > env.yaml # e.g., run this after installing new dependencies locally
|
|
diff environments/flowdock_environment.yaml env.yaml # note the differences and copy accepted changes back into e.g., `environments/flowdock_environment.yaml`
|
|
rm env.yaml # clean up temporary environment file
|
|
```
|
|
|
|
</details>
|
|
|
|
## Docker
|
|
|
|
<details>
|
|
|
|
Given that this tool has a number of dependencies, it may be easier to run it in a Docker container.
|
|
|
|
Pull from [Docker Hub](https://hub.docker.com/repository/docker/cford38/flowdock): `docker pull cford38/flowdock:latest`
|
|
|
|
Alternatively, build the Docker image locally:
|
|
|
|
```bash
|
|
docker build --platform linux/amd64 -t flowdock .
|
|
```
|
|
|
|
Then, run the Docker container (and mount your local `checkpoints/` directory)
|
|
|
|
```bash
|
|
docker run --gpus all -v ./checkpoints:/software/flowdock/checkpoints --rm --name flowdock -it flowdock /bin/bash
|
|
|
|
# docker run --gpus all -v ./checkpoints:/software/flowdock/checkpoints --rm --name flowdock -it cford38/flowdock:latest /bin/bash
|
|
```
|
|
|
|
</details>
|
|
|
|
## Acknowledgements
|
|
|
|
`FlowDock` builds upon the source code and data from the following projects:
|
|
|
|
- [DiffDock](https://github.com/gcorso/DiffDock)
|
|
- [lightning-hydra-template](https://github.com/ashleve/lightning-hydra-template)
|
|
- [NeuralPLexer](https://github.com/zrqiao/NeuralPLexer)
|
|
|
|
We thank all their contributors and maintainers!
|
|
|
|
## License
|
|
|
|
This project is covered under the **MIT License**.
|
|
|
|
## Citing this work
|
|
|
|
If you use the code or data associated with this package or otherwise find this work useful, please cite:
|
|
|
|
```bibtex
|
|
@inproceedings{morehead2025flowdock,
|
|
title={FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction},
|
|
author={Alex Morehead and Jianlin Cheng},
|
|
booktitle={Intelligent Systems for Molecular Biology (ISMB)},
|
|
year=2025,
|
|
}
|
|
```
|