List of Molecular and Material design using Generative AI and Deep Learning

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

Molecular Processing Made Easy.

AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.

A Euclidean diffusion model for structure-based drug design.

Interaction Fingerprints for protein-ligand complexes and more

DiffLinker: Equivariant 3D-Conditional Diffusion Model for Molecular Linker Design

Official code repository of < CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph >

Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.

The official implementation of 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (ICLR 2023)

Interface for AutoDock, molecule parameterization

TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design

A python package for exploring end-to-end chemistry workflows on quantum computers and simulators.

benchmark dataset and Deep learning method (Hierarchical Interaction Network, HINT) for clinical trial approval probability prediction, published in Cell Patterns 2022.

DrugGPT: A GPT-based Strategy for Designing Potential Ligands Targeting Specific Proteins

Python for chemoinformatics

MaSIF-neosurf: surface-based protein design for ternary complexes.

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

Mixed continous/categorical flow-matching model for de novo molecule generation.

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