TwoFold: Highly accurate structure and affinity prediction for protein-ligand complexes from sequences

Hsu, Darren J.; Lu, Hao; Kashi, Aditya; Matheson, Michael; Gounley, John; Wang, Feiyi; Joubert, Wayne; Glaser, Jens

doi:10.1177/10943420231201151

TwoFold: Highly accurate structure and affinity prediction for protein-ligand complexes from sequences

Journal Article · 29 October 2023 · International Journal of High Performance Computing Applications

DOI:https://doi.org/10.1177/10943420231201151· OSTI ID:2205431

Hsu, Darren J. ^[1]; Lu, Hao ^[1]; ^[1]; Matheson, Michael ^[1]; ^[1]; ^[1]; ^[1]; ^[1]

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). National Center for Computational Sciences (NCCS)

We describe our development of ab initio protein-ligand binding pose prediction models based on transformers and binding affinity prediction models based on the neural tangent kernel (NTK). Folding both protein and ligand, the TwoFold models achieve efficient and quality predictions matching state-of-the-art implementations while additionally reconstructing protein structures. In conclusion, solving NTK models points to a new use case for highly optimized linear solver benchmarking codes on HPC.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2205431

Journal Information:: International Journal of High Performance Computing Applications, Journal Name: International Journal of High Performance Computing Applications Journal Issue: 6 Vol. 37; ISSN 1094-3420

Publisher:: SAGECopyright Statement

Country of Publication:: United States

Language:: English

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