Hybrid AI/physics pipeline for miniprotein binder prioritization: application to the BRD3 ET domain

AI-based protein design can rapidly generate thousands of candidate binders, but most fail to fold or bind productively, creating a critical need for robust prioritization. We present a generalizable hybrid pipeline that integrates deep-learning design and physics-based simulations to filter large libraries down to a handful of high-confidence candidates.

• Publication year

  2025

• Venue

  Chemical Communications

• Publication date

  2025-11-10

• Fields of study

  Chemistry, Medicine, Computer Science, Biology

• Identifiers
  DOI 10.1039/d5cc05032dPMID 41211640PMCID 12598730
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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