Deep Learning of CYP450 Binding of Small Molecules by Quantum Information.

Abstract

Drug-drug interaction can lead to diminished therapeutic effects or increased toxicity, posing significant risks, especially in polypharmacy, and cytochrome P450 plays an indispensable role in this interaction. Cytochrome P450, responsible for the metabolism and detoxification of most drugs, metabolizes about 90% of Food and Drug Administration-approved drugs, making early detection of potential drug-drug interactions. Over the years, in-silico modeling has become a valuable tool for predicting drug-drug interactions. Still, conventional molecular descriptors focusing on the structural properties of drugs often overlook complex electronic interactions critical for accurate predictions. To address this, we implemented the Manifold Embedding of Molecular Surface (MEMS) approach, which retains the quantum mechanical characteristics of molecules. MEMS-generated electronic attributes were embedded and featurized for deep learning using the DeepSets architecture, where our models achieved high accuracy, particularly for cytochrome P450 enzyme 1A2 (CYP1A2), with F1 scores reaching up to 0.866. This study highlights the potential of integrating detailed electronic properties with deep learning to improve predictive models for drug-drug interactions, addressing the limitations of traditional molecular descriptors and machine-learning techniques.