Machine Learning-Assisted Iterative Screening for Efficient Detection of Drug Discovery Starting Points.

Abstract

High-throughput screening (HTS) remains central to small molecule lead discovery, but increasing assay complexity challenges the screening of large compound libraries. While retrospective studies have assessed active-learning-guided screening, extensive prospective validations are rare. Here, we report the first prospective evaluation of machine learning (ML)-assisted iterative HTS in a large-scale drug discovery project. Using a mass spectrometry-based assay for salt-inducible kinase 2, we screened just 5.9% of a two million-compound library in three batches and recovered 43.3% of all primary actives identified in a parallel full HTS─including all but one compound series selected by medicinal chemists. This demonstrates that ML-guided iterative screening can significantly reduce the experimental cost while maintaining hit discovery quality. Retrospective benchmarks further showed that the ML approach outperforms similarity-based methods in hit recovery and chemical space coverage. In summary, this study highlights the potential of ML-driven iterative HTS to improve efficiency also in large-scale drug discovery projects.