FastDTI: Drug-Target Interaction Prediction using Multimodality and Transformers

Mathijs Boezer, Maryam Tavakol, Zahra Sajadi

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.