Combating small-molecule aggregation with machine learning

Journal Article (Journal Article)

Biological screens are plagued by false-positive hits resulting from aggregation. Methods to triage small colloidally aggregating molecules (SCAMs) are in high demand. Herein, we disclose a neural network to flag such entities. Our data demonstrate the utility of machine learning for predicting SCAMs, achieving 80% of correct predictions in an out-of-sample evaluation. The tool is competitive with a panel of expert chemists, who correctly predict 61% ± 7% of the same molecules in a Turing-like test. Our computational routine provides insight into features governing aggregation that had remained hidden to expert intuition. Further, we quantify that up to 15%–20% of ligands in publicly available chemogenomic databases have high potential to aggregate at a typical screening concentration (30 μM), imposing caution in systems biology and drug design programs. Our approach provides a means to augment human intuition and mitigate attrition and a pathway to accelerate future molecular medicine.

Full Text

Duke Authors

Cited Authors

  • Lee, K; Yang, A; Lin, YC; Reker, D; Bernardes, GJL; Rodrigues, T

Published Date

  • September 22, 2021

Published In

Volume / Issue

  • 2 / 9

Electronic International Standard Serial Number (EISSN)

  • 2666-3864

Digital Object Identifier (DOI)

  • 10.1016/j.xcrp.2021.100573

Citation Source

  • Scopus