drug-discovery

Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 175,000+ scientists worldwide. 163 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.

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

Message Passing Neural Networks for Molecule Property Prediction

An AI co-scientist running on your desktop. Claude Science but better.

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

ScienceClaw is a personal research assistant built with LangChain DeepAgents and AIO Sandbox infrastructure, adopting a completely new architecture beyond OpenClaw. It offers stronger security, better transparency, and a more user-friendly experience.

This repo contains the codes for our paper "End-to-End Full-Atom Antibody Design"

Codes for our paper "Full-Atom Peptide Design with Geometric Latent Diffusion" (NeurIPS 2024)

DrugGPT: A GPT-based Strategy for Designing Potential Ligands Targeting Specific Proteins

Codebase and CLI for PLAPT: A state-of-the-art protein-ligand binding affinity model for drug discovery

SELFormer: Molecular Representation Learning via SELFIES Language Models

DrugHIVE: Structure-based drug design with a deep hierarchical generative model

🦀 Agentic RAG for drug intelligence · 57 skills · 15 task categories · DTI · ADR · DDI · PGx · Repurposing · Powered by LangGraph

This repo contains the codes for our paper Conditional Antibody Design as 3D Equivariant Graph Translation.

Training and inference code for ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design [ICLR 2025 oral]

Implementation of ICLR23 paper "Molecule Generation for Target Protein Binding with Structural Motifs"

Python-based GUI to collect Feedback of Chemist in Molecules