Repository Index

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

• drug-discovery
• quantum-chemistry
• deep-learning
• biology

Official repository for the Boltz biomolecular interaction models

• binding-prediction
• drug-discovery
• protein-structure

A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)

• drug-repurposing
• deep-learning
• drug-target-interactions
• toolkit

Python package for graph neural networks in chemistry and biology

• deep-learning
• graph-neural-networks
• dgl
• cheminformatics

MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction (Bioinformatics)

A Network Integration Approach for Drug-Target Interaction Prediction

LLM for Drug Editing, ICLR 2024

• chatgpt
• conversation
• drug
• drug-discovery

Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.

• disentanglement
• drug-discovery
• genomics
• perturbation

TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments(BIOINFORMATICS 2020) https://doi.org/10.1093/bioinformatics/btaa524

• self-attention
• compound-protein-interaction
• transformercpi
• structure-free-virtual-sreening

Interpretable bilinear attention network with domain adaptation improves drug-target prediction.

Rails frontend to The Genome Institute's drug gene interaction database.

TypeDB Bio: Biomedical Knowledge Graph

• bioinformatics
• knowledge-graph
• knowledge-base
• drug-discovery

NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions

• computational-biology
• bioinformatics
• graph-convolution
• deep-learning

CancerFoundation: A single-cell RNA sequencing foundation model to decipher drug resistance in cancer

• cancer
• foundation-model
• single-cell

PyTorch implementation of "TGSA: Protein-Protein Association-Based Twin Graph Neural Networks for Drug Response Prediction with Similarity Augmentation"

Interpretable modeling of cellular responses to drug perturbations using cycle consistence learning

DeepAEG: A model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies

Interpretable drug response prediction with graph neural networks over drugs, cell lines, and genes.

GNN-based gene-selection deep learning model for drug response prediction