DrugMAP: Deep Multimodal Transformers for Drug-Target Mechanism of Action Prediction

Article Type

Research Article

Publication Title

IEEE Transactions on Artificial Intelligence

Abstract

The development of new drugs is an expensive and time-consuming process, often hindered by the lack of reliable models to predict drug-target interactions (DTIs) and their mechanisms of action (MoA). Existing deep learning-based methods for DTI prediction typically focus only on binary classification of interactions, overlooking the complex mechanisms underlying these interactions. Moreover, the absence of comprehensive datasets for modeling MoA further complicates this task. To address these limitations, we introduce DrugMAP, a novel multimodal deep learning model that integrates graph neural networks and transformer-based architectures to predict both DTIs and their MoA. We construct a large-scale dataset from multiple public sources, adding a new level of complexity by including detailed MoA annotations for thousands of drug-target pairs. DrugMAP simultaneously leverages the molecular and atomic-level structures of drugs and target proteins, utilizing multirepresentational encoders for enhanced feature extraction. Experimental results show that DrugMAP outperforms state-of-the-art models for both DTI and MoA prediction across multiple benchmark datasets. Our model achieves a 3.5% improvement in AUC for MoA prediction, demonstrating its potential for guiding drug discovery and understanding adverse drug events.

First Page

3087

Last Page

3099

DOI

10.1109/TAI.2025.3565671

Publication Date

1-1-2025

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