Popular GitHub Repositories for Molecule Gene

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

Message Passing Neural Networks for Molecule Property Prediction

Python package for graph neural networks in chemistry and biology

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

Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch

EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein

RT-GENE: Real-Time Eye Gaze and Blink Estimation in Natural Environments

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

Scientific Large Language Models: A Survey on Biological & Chemical Domains

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

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

[ICLR 2024] Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models

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

Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval, Nat Mach Intell 2023 (https://www.nature.com/articles/s42256-023-00759-6)

[ICLR 2023] One Transformer Can Understand Both 2D & 3D Molecular Data (official implementation)

Pre-training Molecular Graph Representation with 3D Geometry, ICLR'22 (https://openreview.net/forum?id=xQUe1pOKPam)

[ICLR 2024] Domain-Agnostic Molecular Generation with Chemical Feedback

Training and evaluating a variational autoencoder for pan-cancer gene expression data

A Python library for Gene–environment interaction analysis via deep learning