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bioRxiv · 10.1101/2023.03.14.532563

LEP-AD: Language Embedding of Proteins and Attention to Drugs predicts drug target interactions

Daga, A.·Khan, S. A.·Cabrero, D. G.·Hoehndorf, R.·Kiani, N.·Tegner, J.
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Abstract

AO_SCPLOWBSTRACTC_SCPLOWPredicting drug-target interactions is a tremendous challenge for 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 (the LED-AD) outperforms a model using an explicit alpha-fold 3D representation of proteins (e.g., LEP-AD supervised by Alphafold). The LEP-AD model scales favorably in performance with the size of training data. Code available at https://github.com/adaga06/LEP-AD

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Daga, A., Khan, S. A., Cabrero, D. G., Hoehndorf, R., Kiani, N., Tegner, J.. 2023-03-15. LEP-AD: Language Embedding of Proteins and Attention to Drugs predicts drug target interactions. https://doi.org/10.1101/2023.03.14.532563

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