SCAM Detective: Accurate Predictor of Small, Colloidally Aggregating Molecules

Small,
colloidally aggregating molecules (SCAMs) are the most common
source of false positives in high-throughput screening (HTS) campaigns.
Although SCAMs can be experimentally detected and suppressed by the
addition of detergent in the assay buffer, detergent sensitivity is
not routinely monitored in HTS. Computational methods are thus needed
to flag potential SCAMs during HTS triage. In this study, we have
developed and rigorously validated quantitative structure-interference
relationship (QSIR) models of detergent-sensitive aggregation in several
HTS campaigns under various assay conditions and screening concentrations.
In particular, we have modeled detergent-sensitive aggregation in
an AmpC β-lactamase assay, the preferred HTS counter-screen
for aggregation, as well as in another assay that measures cruzain
inhibition. Our models increase the accuracy of aggregation prediction
by ∼53% in the β-lactamase assay and by ∼46% in
the cruzain assay compared to previously published methods. We also
discuss the importance of both assay conditions and screening concentrations
in the development of QSIR models for various interference mechanisms
besides aggregation. The models developed in this study are publicly
available for fast prediction within the SCAM detective web application
(https://scamdetective.mml.unc.edu/).