Authors: Darren J. Hsu, Hao Lu, Aditya Kashi, Michael Matheson, John Gounley, Feiyi Wang, Wayne Joubert, and Jens Glaser (Oak Ridge National Laboratory (ORNL))
Abstract: 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. NTK and Gaussian Process models are demonstrated to be a worthy use of HPC resources for AI, and the advantages of adapting highly-optimized linear solver benchmarking codes to solve the large dense linear systems required by these models are shown.
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