FastDTI: Drug-Target Interaction Prediction using Multimodality and Transformers

Abstract

Recent advances in machine learning have proved effective in the application of drug discovery by predicting the drugs that are likely to interact with a protein target of a certain disease, leading to prioritizing drug development and re-purposing efforts. State-of-the-art techniques in Drug-Target Interaction (DTI) prediction are often computationally expensive and can only be trained on small specialized datasets. In this paper, we propose a novel architecture, called FastDTI, utilizing pretrained transformers and graph neural networks in a self-supervised manner on large-scale (unlabeled) data, which additionally allows for embedding of multimodal input representations, for both drug and protein properties. Extensive empirical study demonstrates that our approach outperforms state-of-the-art DTI methods on the KIBA benchmark dataset, while greatly improving the computational complexity of training, about 200 times faster, leading to excellent performance results.