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

Message Passing Neural Networks for Molecule Property Prediction

• machine-learning
• chemistry
• neural-networks
• drug-discovery

Chai-1, SOTA model for biomolecular structure prediction

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

• machine-learning
• generative-models
• drug-discovery
• benchmark

Python package for graph neural networks in chemistry and biology

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

A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)

• deep-learning
• pytorch
• deep-chemistry
• graph-neural-network

OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research

• computational-chemistry
• computational-biology
• deep-learning
• deep-neural-networks

Molecular Processing Made Easy.

• python
• molecule
• rdkit
• drug-discovery

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

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

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

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

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

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