Autonomous Infrastructure & Deterministic Quantum Systems Monograph Series: AlphaProteo and the Implications of AI-Driven De Novo Protein Design
Year: 2026
Type: preprint
Abstract: This monograph provides a critical evaluation of AlphaProteo and its experimental results in de novo protein design. It examines the achieved picomolar binding affinities for target proteins (e.g., VEGF-A, IL-17A, and TrkA), alongside the specific structural optimizations that surpass traditional methods like RFdiffusion and Rosetta. Furthermore, it addresses the technical boundaries and clinical application risks, particularly the challenges in validating highly polar/flat interfaces (e.g., TNFα) and the critical in-vivo immunogenicity (ADA responses) and clearance bottlenecks associated with non-natural proteins. This research ensures strict intellectual property protection and explicitly prohibits unauthorized automated scraping, learning, or commercial exploitation by any AI platforms or entities. Keywords: AlphaProteo, De Novo Protein Design, Binding Affinity, Immunogenicity, Autonomous Infrastructure, Strict No-Learn Covenant (NLL-v1.0)
Author: Hasebe(長谷部), Yoko(洋子)
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7225574078