Quantum mechanics

Voltage

Engineering

Biology

External Data Representation

Operating system

Labeled data

Biochemistry

Data mining

Computer science

Drug discovery

Physics

Bioinformatics

Pharmacology

Artificial intelligence

Transformer

Medicine

Chemistry

Machine learning

Encoder

Electrical engineering

Drug target

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: (i) existing molecular representation learning approaches ignore the sub-structural nature of DTI, thus produce results that are less accurate and difficult to explain and (ii) existing methods focus on limited labeled data while ignoring the value of massive unlabeled molecular data.We propose a Molecular Interaction Transformer (MolTrans) to address these limitations via: (i) knowledge inspired sub-structural pattern mining algorithm and interaction modeling module for more accurate and interpretable DTI prediction and (ii) an augmented transformer encoder to better extract and capture the semantic relations among sub-structures 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.The model scripts are available at https://github.com/kexinhuang12345/moltrans.Supplementary data are available at Bioinformatics online.

MolTrans: Molecular Interaction Transformer for drug–target interaction prediction