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This is the official repo of paper: Multi-modal Protein-Drug Interaction Prediction Via Attention-based Network. Code and pretrained model weights are available here.

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brian-zZZ/PDI_fusionner

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Multi-modal Protein-Drug Interaction Prediction Via Attention-based Network

A novel attention mechanism-driven deep model for estimating the binding affinity between protein and drug by exploiting multi-modal information including protein sequence, protein structure, and drug fingerprint.

  • Our pre-trained encoder is at ./files/pretrain/BertModel.pth.
  • Code of fine-tuning phase is released.

Table of Contents

Environment & Dependencies

  • Python==3.8.13

  • torch==1.8.0

# create new environ
conda create -n pdi_fusionner python=3.8.13

# build dependencies
pip install -r requirements.txt
pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html

Usage

  1. Data preparation
  • Place the processed data file, namely pafnucy_total_rdkit-smiles-v1.csv, to ./files/data. Notice that this file combines both the protein datasets, i.e., PDBbind, CASF-2013 and Astex datasets, and the drug SMILES dataset. An entry of this file should look like:

    PDB-ID seq SMILES rdkit_smiles Affinity-Value set
    0 11gs PYTVVYF... CCC(=O)... CC[C@@H]... 5.82 train
  • Place the protein contact map that processed from 3D crystal structure data, namely pdbbind_2016, to ./files/3d_pdb

  • Update the configurations config.yaml to make sure everything is matched

  1. Fine-tuning
python finetune_main.py --model_idx=0 --epochs=150 --gpu_start=0 --batch_size=64 \
                        --patience=10 --factor=0.5 --min_lr=1e-5  --tb

Specify model_idx to choose our proposed model or its ablation variants. More options please refer to config.yaml and the argument parser in finetune_main.py.

Addition

More detailed information will be released and updated soon!

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This is the official repo of paper: Multi-modal Protein-Drug Interaction Prediction Via Attention-based Network. Code and pretrained model weights are available here.

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