Self-focusing virtual screening with active design space pruning

TL;DR

This paper introduces a design space pruning method for virtual screening that reduces inference costs by eliminating poor candidates early, maintaining performance while lowering computational overhead.

Contribution

It extends model-guided optimization with an irreversible pruning technique to decrease inference costs in large-scale virtual screening tasks.

Findings

DSP significantly reduces overhead costs.

Performance remains comparable to baseline methods.

Applicable to various optimization tasks.

Abstract

High-throughput virtual screening is an indispensable technique utilized in the discovery of small molecules. In cases where the library of molecules is exceedingly large, the cost of an exhaustive virtual screen may be prohibitive. Model-guided optimization has been employed to lower these costs through dramatic increases in sample efficiency compared to random selection. However, these techniques introduce new costs to the workflow through the surrogate model training and inference steps. In this study, we propose an extension to the framework of model-guided optimization that mitigates inferences costs using a technique we refer to as design space pruning (DSP), which irreversibly removes poor-performing candidates from consideration. We study the application of DSP to a variety of optimization tasks and observe significant reductions in overhead costs while exhibiting similar performance to the baseline optimization. DSP represents an attractive extension of model-guided optimization that can limit overhead costs in optimization settings where these costs are non-negligible relative to objective costs, such as docking.