Summary
This is a diffusion-based deep learning tool for molecular docking that predicts 3D binding poses of ligands to protein targets. You'd use it for structure-based drug design when you need to know where a molecule binds, not how strongly it binds. It handles PDB files or protein sequences via ESMFold, works with SMILES or structure files, and can process single complexes or batch virtual screening campaigns. The confidence scores tell you how certain the model is about the pose, not binding affinity, so you'll want to pair it with GNINA or MM/GBSA for affinity estimates. GPU recommended since it's 10-100x faster than CPU. First run takes a few minutes to pre-compute lookup tables.
Install to Claude Code
npx -y skills add davila7/claude-code-templates --skill diffdock --agent claude-codeInstalls into .claude/skills of the current project.
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Plug Mailtrap into your AI workflow and let it handle the email.
Connect Mailtrap MCP →Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →Make coding agent sessions - Searchable, Shareable, Vendor-neutral & Scored.
Try For Free →Your agent targets a perfect 10 Code Health score. Deterministic. Every commit.
Try For Free →Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →belt cli automatically finds the best tools and skills for your agent. image, video, music, tts...
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