Combating small molecule aggregation with machine learning
Abstract
Biological screens are plagued by false positive hits resulting from aggregation. Thus, methods to triage small colloidally aggregating molecules (SCAMs) are in high demand. Herein, we disclose a bespoke machine-learning tool to confidently and intelligibly flag such entities. Our data demonstrate an unprecedented utility of machine learning for predicting SCAMs, achieving 80% of correct predictions in a challenging out-of-sample validation. The tool outperformed a panel of expert chemists, who correctly predicted 61 +/- 7% of the same test molecules in a Turing-like test. Further, the computational routine provided insight into molecular features governing aggregation that had remained hidden to expert intuition. Leveraging our tool, we quantify that up to 15-20% of ligands in publicly available chemogenomic databases have the high potential to aggregate at typical screening concentrations, imposing caution in systems biology and drug design programs. Our approach provides a means to augment human intuition, mitigate attrition and a pathway to accelerate future molecular medicine.
Cite
@article{arxiv.2105.00267,
title = {Combating small molecule aggregation with machine learning},
author = {Kuan Lee and Ann Yang and Yen-Chu Lin and Daniel Reker and Goncalo J. L. Bernardes and Tiago Rodrigues},
journal= {arXiv preprint arXiv:2105.00267},
year = {2021}
}