Analysis of Biological Screening Compounds with Single- or Multi-Target Activity via Diagnostic Machine Learning.

ABSTRACT: Predicting compounds with single- and multi-target activity and exploring origins of compound specificity and promiscuity is of high interest for chemical biology and drug discovery. We present a large-scale analysis of compound promiscuity including two major components. First, high-confidence datasets of compounds with multi- and corresponding single-target activity were extracted from biological screening data. Positive and negative assay results were taken into account and data completeness was ensured. Second, these datasets were investigated using diagnostic machine learning to systematically distinguish between compounds with multi- and single-target activity. Models built on the basis of chemical structure consistently produced meaningful predictions. These findings provided eviden

SUBMITTER: Feldmann C 

PROVIDER: S-EPMC7761051 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature