Repository Index

A Network Integration Approach for Drug-Target Interaction Prediction

A Python package for molecular docking with an extensive, highly-curated dataset and a set of realistic benchmark tasks for drug discovery.

ChemML is a machine learning and informatics program suite for the chemical and materials sciences.

• quantum-mechanics
• materials-informatics
• data-science
• machine-learning

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

Optimising chemical reactions using machine learning

• chemistry
• machine-learning
• optimization
• bayesian-optimization

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

We would like to maintain a list of resources which aim to solve molecular docking and other closely related tasks.

• ai-aided-drug-discovery
• awesome
• drug-discovery
• molecular-docking

Readings for "A Unified View of Relational Deep Learning for Drug Pair Scoring." (IJCAI 2022)

• ddi
• drug-synergy
• synergy-prediction
• deep-chemistry

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

TypeDB Bio: Biomedical Knowledge Graph

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

A database of paths that represent the mechanism of action from a drug to a disease in an indication.

• ncats-translator

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

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

Multi-stage Riemannian flow matching for physically valid molecular docking, with GNINA scoring, PoseBusters filtering, CLI inference, and benchmarks.

• alphafold3
• docking
• drug-design
• drug-discovery

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

• cancer
• foundation-model
• single-cell

Exploring the space of drug combinations to discover synergistic drugs using Active Learning

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

UV-first benchmark for protein-ligand docking with reproducible annotation, pocket similarity, and HPC workflows.

• benchmark
• bioinformatics
• cheminformatics
• computational-chemistry

Generative models for transcriptomic-driven or protein-driven molecular design (PaccMann^RL).

• deep-reinforcement-learning
• generative-model
• vae
• molecule-generation

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

dlang sdl2 opengl wrappers

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.