Ultrahigh-Throughput Virtual Screening Strategies against PPI Targets: A Case Study of STAT Inhibitors.

ABSTRACT: In recent years, virtual screening of ultralarge (10⁸⁺) libraries of synthetically accessible compounds (uHTVS) became a popular approach in hit identification. With AI-assisted virtual screening workflows, such as Deep Docking, these protocols might be feasible even without supercomputers. Yet, these methodologies have their own conceptual limitations, including the fact that physics-based docking is replaced by a cheaper deep learning (DL) step for the vast majority of compounds. In turn, the performance of this DL step will highly depend on the performance of the underlying docking model that is used to evaluate parts of the whole data set to train the DL architecture itself. Here, we evaluated the performance of the popular Deep Docking workflow on compound libraries of diff

SUBMITTER: Szalai TV 

PROVIDER: S-EPMC12308805 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature