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Showing 1–5 of 5 results
Advanced filters: Author: Hahnbeom Park Clear advanced filters

  • Here the authors present DeepAccNet, a deep learning framework that estimates per-residue accuracy and residue-residue distance signed error in protein models, which are used to guide Rosetta protein structure refinement. Benchmarking suggests an improvement of accuracy prediction and refinement compared to other related state of the art methods.

    • Naozumi Hiranuma
    • Hahnbeom Park
    • David Baker
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-11

  • The authors in this work introduce RosettaVS, an AI-accelerated open-source drug discovery platform. They apply this tool to multi-billion compound libraries, where it was able to identify compounds that bind important targets KLHDC2 and NaV1.7.

    • Guangfeng Zhou
    • Domnita-Valeria Rusnac
    • Frank DiMaio
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-14

  • The elucidation of general principles for designing β-barrels enables the de novo creation of fluorescent proteins.

    • Jiayi Dou
    • Anastassia A. Vorobieva
    • David Baker
    Research
    Nature
    Volume: 561, P: 485-491