  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.
