Machine Learning-Driven Data Valuation for Optimizing High-Throughput Screening Pipelines.

ABSTRACT: In the rapidly evolving field of drug discovery, high-throughput screening (HTS) is essential for identifying bioactive compounds. This study introduces a novel application of data valuation, a concept for evaluating the importance of data points based on their impact, to enhance drug discovery pipelines. Our approach improves active learning for compound library screening, robustly identifies true and false positives in HTS data, and identifies important inactive samples in an imbalanced HTS training, all while accounting for computational efficiency. We demonstrate that importance-based methods enable more effective batch screening, reducing the need for extensive HTS. Machine learning models accurately differentiate true biological activity from assay artifacts, streamlining the drug di

SUBMITTER: Hesse J 

PROVIDER: S-EPMC11558681 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature