LEP-AD: Language Embeddings of Proteins and Attention to Drugs Predicts Drug Target Interactions

Abstract

Predicting drug-target interactions is an outstanding challenge relevant to drug development and lead optimization. Recent advances include training algorithms to learn drug-target interactions from data and molecular simulations. Here we utilize Evolutionary Scale Modeling (ESM-2) models to establish a Transformer protein language model for drug-target interaction predictions. Our architecture, LEP-AD, combines pre-trained ESM-2 and Transformer-GCN models predicting binding affinity values. We report new best in class state-of-the-art results compared to competing methods such as SimBoost, DeepCPI, Attention-DTA, GraphDTA, and more using multiple datasets, including Davis, KIBA, DTC, Metz, ToxCast, and STITCH. Finally, we find that a pre-trained model with embedding of proteins, as in our LED-AD, outperforms a model using an explicit alpha-fold 3D representation of proteins. The LEP-AD model scales favourably in performance with the size of training data. Code available at https://github.com/adaga06/LEP-AD