Mitigating Molecular Aggregation in Drug Discovery with Predictive
  Insights from Explainable AI
Hunter Sturm Jonas Teufel Kaitlin A. Isfeld Pascal Friederich Rebecca L. Davis
As the importance of high-throughput screening (HTS) continues to grow due to
its value in early stage drug discovery and data generation for training
machine learning models, there is a growing need for robust methods for
pre-screening compounds to identify and prevent false-positive hits. Small,
colloidally aggregating molecules are one of the primary sources of
false-positive hits in high-throughput screens, making them an ideal candidate
to target for removal from libraries using predictive pre-screening tools.
However, a lack of understanding of the causes of molecular aggregation
introduces difficulty in the development of predictive tools for detecting
aggregating molecules. Herein, we present an examination of the molecular
features differentiating datasets of aggregating and non-aggregating molecules,
as well as a machine learning approach to predicting molecular aggregation. Our
method uses explainable graph neural networks and counterfactuals to reliably
predict and explain aggregation, giving additional insights and design rules
for future screening. The integration of this method in HTS approaches will
help combat false positives, providing better lead molecules more rapidly and
thus accelerating drug discovery cycles.