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Sample-efficient Generative Molecular Design using Memory Manipulation
EquiTriton is a project that seeks to implement high-performance kernels for commonly used building blocks in equivariant neural networks, enabling compute efficient training and inference.
JAX implementation of the JKOnet* architecture presented in "Learning Diffusion at Lightspeed".
A trivial demo of chemical regioselectivity prediction via machine learning
EPFL CH-457 "AI for chemistry"
List of Molecular and Material design using Generative AI and Deep Learning
De novo drug design with deep interactome learning
All graph/GNN papers accepted at the International Conference on Machine Learning (ICML) 2024.
Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning
ETHmodlab / dragonfly_gen
Forked from atzkenneth/dragonfly_genDe novo drug design with deep interactome learning
Official implementation of pre-training via denoising for TorchMD-NET
A Euclidean diffusion model for structure-based drug design.
a curated list of resources for everyone interested in learning about digital chemistry
A JAX research toolkit for building, editing, and visualizing neural networks.
A complete computer science study plan to become a software engineer.
[ICLR 2024] "Latent 3D Graph Diffusion" by Yuning You, Ruida Zhou, Jiwoong Park, Haotian Xu, Chao Tian, Zhangyang Wang, Yang Shen
Practical Cheminformatics Tutorials
Awesome papers related to generative molecular modeling and design.
code for "Generalized Schrödinger Bridge Matching" (ICLR 2024).
Bare-bones implementations of some generative models in Jax: diffusion, normalizing flows, consistency models, flow matching, (beta)-VAEs, etc
Fast protein backbone generation with SE(3) flow matching.
A summary of related works about flow matching, stochastic interpolants
AlphaFold Meets Flow Matching for Generating Protein Ensembles
TorchCFM: a Conditional Flow Matching library
FoldFlow: SE(3)-Stochastic Flow Matching for Protein Backbone Generation