Ultrahigh Throughput Protein-Ligand Docking with Deep Learning.
Clyde A.
Methods in molecular biology (Clifton, N.J.). 2022; 2390(): 301-319

Abstract

Ultrahigh-throughput virtual screening (uHTVS) is an emerging field linking together classical docking techniques with high-throughput AI methods. We outline mechanistic docking models' goals and successes. We present different AI accelerated workflows for uHTVS, mainly through surrogate docking models. We showcase a novel feature representation technique, molecular depictions (images), as a surrogate model for docking. Along with a discussion on analyzing screens using regression enrichment surfaces at the tens of billion scale, we outline a future for uHTVS screening pipelines with deep learning. CI - (c) 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.



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