MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction

Abstract

Drug target interaction (DTI) prediction is a foundational task for in silico drug discovery, which is costly and time-consuming due to the need of experimental search over large drug compound space. Recent years have witnessed promising progress for deep learning in DTI predictions. However, the following challenges are still open: (1) the sole data-driven molecular representation learning approaches ignore the sub-structural nature of DTI, thus produce results that are less accurate and difficult to explain; (2) existing methods focus on limited labeled data while ignoring the value of massive unlabelled molecular data. We propose a Molecular Interaction Transformer (MolTrans) to address these limitations via: (1) knowledge inspired sub-structural pattern mining algorithm and interaction modeling module for more accurate and interpretable DTI prediction; (2) an augmented transformer encoder to better extract and capture the semantic relations among substructures extracted from massive unlabeled biomedical data. We evaluate MolTrans on real world data and show it improved DTI prediction performance compared to state-of-the-art baselines.

arXiv commentBioinformatics, 2020

References in corpus (5)

• DeepDTA: Deep Drug-Target Binding Affinity Prediction
• Modeling polypharmacy side effects with graph convolutional networks
• DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
• Natural Language Inference over Interaction Space
• WideDTA: prediction of drug-target binding affinity

Cited by in corpus (6)

• Transformers in Healthcare: A Survey
• Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction
• Associative Learning Mechanism for Drug-Target Interaction Prediction
• Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Review
• Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties
• Machine Learning Applications for Therapeutic Tasks with Genomics Data