Optimal decision-making in high-throughput virtual screening pipelines

Woo, Hyun-Myung; Qian, Xiaoning; Tan, Li; Jha, Shantenu; Alexander, Francis J.; Dougherty, Edward R.; Yoon, Byung-Jun

doi:10.1016/j.patter.2023.100875

Optimal decision-making in high-throughput virtual screening pipelines

Journal Article · Fri Nov 03 04:00:00 EDT 2023 · Patterns

DOI:https://doi.org/10.1016/j.patter.2023.100875· OSTI ID:2282032

^[1]; Qian, Xiaoning ^[2]; Tan, Li ^[3]; Jha, Shantenu ^[4]; Alexander, Francis J. ^[5]; Dougherty, Edward R. ^[6]; Yoon, Byung-Jun ^[2]

Incheon National University (Korea, Republic of)
Texas A & M Univ., College Station, TX (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States)
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Brookhaven National Laboratory (BNL), Upton, NY (United States); Rutgers Univ., Piscataway, NJ (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States)
Texas A & M Univ., College Station, TX (United States)

Screening large pools of molecular candidates to identify those with specific design criteria or targeted properties is demanding in various science and engineering domains. While a high-throughput virtual screening (HTVS) pipeline can provide efficient means to achieving this goal, its design and operation often rely on experts' intuition, potentially resulting in suboptimal performance. In this paper, we fill this critical gap by presenting a systematic framework that can maximize the return on computational investment (ROCI) of such HTVS campaigns. Based on various scenarios, we empirically validate the proposed framework and demonstrate its potential to accelerate scientific discoveries through optimal computational campaigns, especially in the context of virtual screening.

View Accepted Manuscript (DOE)

Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Science Foundation (NSF); National Research Foundation of Korea (NRF)

Grant/Contract Number:: SC0012704; SC0019303; SC0021352

OSTI ID:: 2282032

Report Number(s):: BNL--225127-2023-JAAM

Journal Information:: Patterns, Journal Name: Patterns Journal Issue: 11 Vol. 4; ISSN 2666-3899

Publisher:: Cell PressCopyright Statement

Country of Publication:: United States

Language:: English

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Optimal decision-making in high-throughput virtual screening pipelines

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