Popular GitHub Repositories for Molecular Sciences

Graphormer is a general-purpose deep learning backbone for molecular modeling.

Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.

A deep learning package for many-body potential energy representation and molecular dynamics

SchNetPack - Deep Neural Networks for Atomistic Systems

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

End-To-End Molecular Dynamics (MD) Engine using PyTorch

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

[IJCAI 2023 survey track]A curated list of resources for chemical pre-trained models

Accurate Neural Network Potential on PyTorch

Analyze the unstructured data with Towhee, such as reverse image search, reverse video search, audio classification, question and answer systems, molecular search, etc.

Training neural network potentials

Implementation of GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation (ICLR 2022).

Protein-protein, protein-peptide and protein-DNA docking framework based on the GSO algorithm

Deep Reinforcement Learning for de-novo Drug Design

An awesome & curated list for Artificial General Intelligence, an emerging inter-discipline field that combines artificial intelligence and computational cognitive sciences.

Original implementation of the paper "SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery" by Shion Honda et al.

Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation

[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Implementation of MolCLR: "Molecular Contrastive Learning of Representations via Graph Neural Networks" in PyG.

torch-molecule is a deep learning package for molecular discovery, designed with an sklearn-style interface for property prediction, inverse design and representation learning.