Popular GitHub Repositories for Chemistry Discovery

? Papers & tech blogs by companies sharing their work on data science & machine learning in production.

State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

Official PyTorch implementation of PTS/PSRN: Fast and efficient symbolic expression discovery through parallelized tree search. Evaluates millions of expressions simultaneously on GPU with automated subtree reuse.

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

Data & AI Notebook templates catalog organized by tools, following the IMO (input, model, output) framework for easy usage and discovery..

PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.

文本挖掘和预处理工具（文本清洗、新词发现、情感分析、实体识别链接、关键词抽取、知识抽取、句法分析等），无监督或弱监督方法

LLM Agent that leverages cheminformatics tools to provide informed responses.

Zero shot vulnerability discovery using LLMs

Message Passing Neural Networks for Molecule Property Prediction

"AI-Researcher: Fully-Automated Scientific Discovery with LLM Agents" & "Open-Sourced Alternative to Google AI Co-Scientist"

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

A package for the sparse identification of nonlinear dynamical systems from data

A powerful and flexible machine learning platform for drug discovery

[CVPR'23] Universal Instance Perception as Object Discovery and Retrieval

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

Self-hosted open-source web gallery to view your photos and videos featuring mobile-friendly, tagging and AI powered image discovery

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

SchNetPack - Deep Neural Networks for Atomistic Systems