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Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 250,000+ scientists worldwide. 177 ready-to-use validated skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
Official repository for the Boltz biomolecular interaction models
Message Passing Neural Networks for Molecule Property Prediction
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Python package for graph neural networks in chemistry and biology
A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research
Molecular Processing Made Easy.
A Euclidean diffusion model for structure-based drug design.
The official implementation of 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (ICLR 2023)
GraphDTA: Predicting drug-target binding affinity with graph neural networks
Source code for "DeepDTA: deep drug-target binding affinity prediction"
A Python package for molecular docking with an extensive, highly-curated dataset and a set of realistic benchmark tasks for drug discovery.
ChemML is a machine learning and informatics program suite for the chemical and materials sciences.
LLM for Drug Editing, ICLR 2024
TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments(BIOINFORMATICS 2020) https://doi.org/10.1093/bioinformatics/btaa524
Interpretable bilinear attention network with domain adaptation improves drug-target prediction.
TypeDB Bio: Biomedical Knowledge Graph
NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions
Multi-stage Riemannian flow matching for physically valid molecular docking, with GNINA scoring, PoseBusters filtering, CLI inference, and benchmarks.
PyTorch implementation of "TGSA: Protein-Protein Association-Based Twin Graph Neural Networks for Drug Response Prediction with Similarity Augmentation"
Generative models for transcriptomic-driven or protein-driven molecular design (PaccMann^RL).
Interpretable modeling of cellular responses to drug perturbations using cycle consistence learning