2021/09/23 by Hyun-Myung Woo, Xiaoning Qian, Woo, Hyun-Myung +11
Decision Sciences · Engineering · Materials Science · #FOS: Computer and information sciences #FOS: Mathematics #Innovative Microfluidic and Catalytic Techniques Innovation #Machine Learning (cs.LG) #Machine Learning in Materials Science #Optimization and Control (math.OC) #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2109.11683
openalex publication_date 2021/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 large size of the search space containing the candidates and the substantial computational cost of high-fidelity property prediction models makes screening practically challenging. In this work, we propose a general framework for constructing and optimizing a 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 (ROCI). Based on both simulated as well as real data, we demonstrate that the proposed optimal HTVS framework can significantly accelerate screening virtually without any degradation in terms of accuracy. Furthermore, it enables an adaptive operational strategy for HTVS, where one can trade accuracy for efficiency.