A Hybrid Physics-Deep Learning Framework for Combinatorial De Novo Design of Small-Molecule Binding Proteins
design metrics. Applying this method to two chemically similar steroids achieved a notable experimental success rate (4/26 designs bind their targets), and NMR structures of two designs are in good agreement with design models. Our generalizable, atomically precise approach offers a robust framework for small-molecule binder design, effectively eliminating the need for high-throughput screening.
Authors
- Connor Galvin
- Tanja Kortemme (ORCID: https://orcid.org/0000-0002-8494-680X)
- Dominic Grisingher
- Amy B. Guo (ORCID: https://orcid.org/0000-0003-4775-9498)
- Maple N. Chen (ORCID: https://orcid.org/0000-0002-8208-3799)
- Divya Kranthi
- Mark JS Kelly
- Simon Kretschmer
- Isabella L Alfonso
Institutions
- University of California, San Francisco (US)
Publication Details
- Journal
- bioRxiv (Cold Spring Harbor Laboratory)
- Published
- 2026-04-15
- DOI
- https://doi.org/10.64898/2026.04.12.717919
- Primary Topic
- Protein Structure and Dynamics
- Type
- article
- Field-Weighted Citation Impact
- 0.00