Optimal decision-making in high-throughput virtual screening pipelines

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

doi:10.1016/j.patter.2023.100875

Optimal decision-making in high-throughput virtual screening pipelines

Patterns (N Y). 2023 Nov 3;4(11):100875. doi: 10.1016/j.patter.2023.100875. eCollection 2023 Nov 10.

Authors

Hyun-Myung Woo¹, Xiaoning Qian^{2

3}, Li Tan³, Shantenu Jha^{3

4}, Francis J Alexander⁵, Edward R Dougherty², Byung-Jun Yoon^{2

3}

Affiliations

¹ Department of Biomedical & Robotics Engineering, Incheon National University, Incheon 22012, Republic of Korea.
² Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
³ Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA.
⁴ Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, USA.
⁵ Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA.

Abstract

The need for efficient computational screening of molecular candidates that possess desired properties frequently arises in various scientific and engineering problems, including drug discovery and materials design. However, the enormous search space containing the candidates and the substantial computational cost of high-fidelity property prediction models make screening practically challenging. In this work, we propose a general framework for constructing and optimizing a high-throughput virtual screening (HTVS) pipeline that consists of multi-fidelity models. The central idea is to optimally allocate the computational resources to models with varying costs and accuracy to optimize the return on computational investment. Based on both simulated and real-world data, we demonstrate that the proposed optimal HTVS framework can significantly accelerate virtual screening without any degradation in terms of accuracy. Furthermore, it enables an adaptive operational strategy for HTVS, where one can trade accuracy for efficiency.

Keywords: HTS; HTVS; ROCI; high-throughput screening; high-throughput virtual screening pipeline; optimal computational campaign; optimal decision-making; optimal screening; return on computational investment.