Optimal decision-making in high-throughput virtual screening pipelines.

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 th

SUBMITTER: Woo HM 

PROVIDER: S-EPMC10682755 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature