{"product_id":"boltz-2-ai-skill-protein-structure-prediction-binding","title":"Boltz-2 AI Skill: Protein Structure Prediction \u0026 Binding","description":"\u003ch3\u003eBoltz-2 AI Skill: Protein Structure Prediction \u0026amp; Binding\u003c\/h3\u003e\n\n\u003cp\u003eThe Boltz-2 AI Skill facilitates the prediction of protein structures, nucleic-acid interactions, and small-molecule complexes. It serves as an alternative to AlphaFold3, offering options for binding-affinity prediction in validating designed binders against a target. This skill is aligned with the open-source principles, with tools and weights freely available via Python's package index PyPI (`boltz`) and GitHub.\u003c\/p\u003e\n\n\u003ch3\u003eWhat this skill does\u003c\/h3\u003e\n\n\u003cul\u003e\n  \u003cli\u003eSupports structure prediction for complex biological entities, including proteins, DNA, RNA, and ligands.\u003c\/li\u003e\n  \u003cli\u003eUses diffusion co-folding, closest in functionality to AlphaFold3, to analyze inputs and produce structural confidence metrics such as pTM\/ipTM\/pLDDT.\u003c\/li\u003e\n  \u003cli\u003eProvides capabilities to co-fold a protein with a SMILES or CCD-described ligand for detailed molecular interaction insights.\u003c\/li\u003e\n  \u003cli\u003eFacilitates binding-affinity prediction, aiding in binder-validation campaigns.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eWho it is for\u003c\/h3\u003e\n\n\u003cul\u003e\n  \u003cli\u003eDevelopers and scientific research teams utilizing AI coding agents like Claude Code, Cursor, and Codex.\u003c\/li\u003e\n  \u003cli\u003eBiotechnology and pharmaceutical researchers working on protein engineering, drug discovery, and molecular interaction studies.\u003c\/li\u003e\n  \u003cli\u003eAcademics and scientists focusing on structural biology and computational chemistry.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eUse cases\u003c\/h3\u003e\n\n\u003cul\u003e\n  \u003cli\u003eValidating the design of protein binders against specific biological targets.\u003c\/li\u003e\n  \u003cli\u003eRunning simulations to co-fold proteins with ligands for drug design research.\u003c\/li\u003e\n  \u003cli\u003eSubstituting closed-source solutions with an open-weight, accessible approach for academic studies and enterprise use.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eTechnical details\u003c\/h3\u003e\n\n\u003cul\u003e\n  \u003cli\u003eIntegrated with GitHub for code and weights distribution (github.com\/jwohlwend\/boltz).\u003c\/li\u003e\n  \u003cli\u003eLeverages a YAML-based configuration for defining the molecular complex and specifying protein, nucleic-acid, and ligand sequences for prediction tasks.\u003c\/li\u003e\n  \u003cli\u003eRequires MSAs for each protein chain, available through external services queried via `--use_msa_server` with `api.colabfold.com`.\u003c\/li\u003e\n  \u003cli\u003eOffers configurable parameters like `--recycling_steps` and `--diffusion_samples` for optimizing structure prediction processes.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c!-- mcpcart:static-blocks:start --\u003e\n\u003chr\u003e\n\u003ch3\u003eSource \u0026amp; Licence\u003c\/h3\u003e\n\u003cp\u003eThis package is built on open-source work published by \u003cstrong\u003eaipoch\u003c\/strong\u003e (\u003ca href=\"https:\/\/github.com\/aipoch\/open-science\" rel=\"nofollow noopener\" target=\"_blank\"\u003eaipoch\/open-science\u003c\/a\u003e) and distributed under \u003cstrong\u003eApache-2.0\u003c\/strong\u003e. The original licence text and copyright notice are included in your download.\u003c\/p\u003e\n\u003cp\u003ePersonal and commercial use, modification and redistribution are permitted under the Apache License 2.0, which also includes an express patent grant. Attribution and any NOTICE file must be retained.\u003c\/p\u003e\n\u003cp\u003eYour purchase covers curation, licence verification, packaging, documentation and instant delivery. It does not grant exclusive rights to the underlying open-source code, which remains available under its original licence.\u003c\/p\u003e\n\u003ch3\u003eDelivery \u0026amp; Support\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eDelivery:\u003c\/strong\u003e instant — a secure download link is emailed to you as soon as payment is confirmed.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFormat:\u003c\/strong\u003e ZIP archive containing the skill files, documentation and the original licence.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSupport:\u003c\/strong\u003e \u003ca href=\"mailto:support@mcpcart.com\"\u003esupport@mcpcart.com\u003c\/a\u003e — we aim to reply within 2 business days.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUpdates:\u003c\/strong\u003e updates are included only where stated on this page.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch3\u003eRefunds\u003c\/h3\u003e\n\u003cp\u003eThis is a digital product delivered immediately after purchase. By completing your order you request immediate delivery and acknowledge that, once the download has been accessed, the statutory right to cancel no longer applies to the extent permitted by law. Refund requests are handled in accordance with our published Refund Policy.\u003c\/p\u003e\n\u003cp style=\"font-size:0.85em;color:#666;\"\u003eClaude, Codex, Gemini and Cursor are trademarks of their respective owners. MCP Cart is an independent marketplace and is not affiliated with, endorsed by, or sponsored by any of them. Compatibility references describe interoperability only.\u003c\/p\u003e\n\u003c!-- mcpcart:static-blocks:end --\u003e","brand":"MCP Cart","offers":[{"title":"Default Title","offer_id":53014676046135,"sku":"MCP-AIPOCH-OPEN-SCIENCE-BOLTZ","price":2.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0981\/3950\/4951\/files\/EmRadFoi83t4dBjk5Bl2_a14a07b8033449ffa8b4b3fccb2905ba.jpg?v=1789898746","url":"https:\/\/mcpcart.com\/products\/boltz-2-ai-skill-protein-structure-prediction-binding","provider":"SPF PRO","version":"1.0","type":"link"}