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
a generalist algorithm for cellular segmentation with human-in-the-loop capabilities
- segmentation
- cell-segmentation
- cell-biology
Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
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
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
- deep-learning
- protein-sequences
- protein-structure
- semi-supervised-learning
Chainer Chemistry: A Library for Deep Learning in Biology and Chemistry
Interactive network visualization in Python and Dash, powered by Cytoscape.js
- plotly-dash
- plotly
- dash
- cytoscape
Deep Learning Library for Single Cell Analysis
A dependency-free cross-platform swiss army knife for PDB files.
Nellie: Automated organelle segmentation, tracking, and hierarchical feature extraction in 2D/3D live-cell microscopy
NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions
MCP server for spatial transcriptomics analysis through natural language interfaces.
3D shape analysis using deep learning
Fast, deterministic cell-type deconvolution for million-bin spatial transcriptomics
Accurate segmentation of bacterial microscope images using deep learning synthetically generated image data.
scPair: Boosting single cell multimodal analysis by leveraging single cell atlases and implicit feature selection
A python package to analyze the matrix fiber orientation and intensity around cells in fiber matrices (as a proxy for cellular forces).
- imaging
- microscopy
- contractility
- collagen