Predicting Metabolic Reactions with a Molecular Transformer for Drug Design Optimization

Metabolism prediction is a crucial step of drug development, as the biotransformations a drug candidate undergoes inside the human body can affect the clinical outcome. Computer-aided drug design has been extensively employed to speed up the process and enhance its efficiency and effectiveness, but among the investigated areas, metabolism has received less attention. This project aimed at leveraging machine learning to analyze large metabolic datasets, make predictions and recognize patterns, in order to fill this knowledge gap and enhance our understanding of metabolism and its impact on drug development. To achieve this goal, we developed a Deep Learning model for metabolism prediction using natural language processing techniques trained on molecular string representations, i.e., Simplified Molecular Input Line Entry Systems (SMILES) strings. To this end, we employ a Molecular Transformer, because of its ability to capture sequential and contextual information within strings (in this case, SMILES) enabling the learning of complex relationships. The transformer was trained using a high quality dataset, MetaQSAR, from which we derived approximately 100 000 instances of metabolic reactions. In this work, we investigate whether the Transformer architecture bears the potential to learn a mapping between the input molecular structures and their corresponding metabolites, in order to expedite drug discovery and improve patient safety.