Explainable Machine Learning for ETR and Drug Chameleonicity

Explainable machine learning that identifies molecular "hot spots" for chameleonicity can guide rapid chemical design for oral absorption of beyond-rule-of-five (bRo5) drugs. Traditional in silico methods rely on computationally intensive 3D physics-based modeling or classical descriptors that do not fully explain bRo5 drug behavior. To address this, we introduced the EPSA-to-TPSA ratio (ETR) as a high-throughput measure of polarity reduction, generating data for thousands of macrocycles, PROTACs, and other bRo5s. Using this data set, we developed an explainable deep learning model to predict EPSA and locate polarity-reducing "hot spots" that influence chameleonicity. This first-of-its-kind interpretable model in the bRo5 3D domain guides chemical modifications before synthesis, helping chemists optimize physicochemical properties and design complex bRo5 drugs with improved oral bioavailability. Model insights validated by molecular dynamics enable robust, high-throughput predictions of bRo5 chameleonic behavior, building on Lipinski descriptors to establish new frameworks for complex drug design.