Leveraging multimodal learning for enhanced drug-target interaction prediction

作者：Guo Chen, Kaixin Sun · 发表于：Frontiers in Pharmacology · 年份：2025 · DOI：10.3389/fphar.2025.1639979 · 被引用次数：3 · 研究领域：Computational Drug Discovery Methods、Machine Learning in Bioinformatics、Cell Image Analysis Techniques

Introduction: The evolving landscape of artificial intelligence in drug discovery necessitates increasingly sophisticated approaches to predict drug-target interactions (DTIs) with high precision and generalizability. In alignment with the current surge of interest in AI-driven pharmacological modeling and integrative biomedical data analysis, this study introduces a multimodal framework for enhancing DTI prediction by fusing heterogeneous data sources. While conventional methods typically rely on unimodal inputs such as chemical structures or protein sequences, they fall short in capturing the complex, multi-faceted nature of biochemical interactions and are often limited in adaptability across different tasks or incomplete datasets. These limitations impede the model's capability to generalize beyond narrow benchmarks and reduce interpretability when modalities are missing or noisy. Methods: To address these challenges, we propose a comprehensive multimodal learning pipeline composed of three principal innovations. The Unified Multimodal Molecule Encoder (UMME) jointly embeds molecular graphs, textual descriptions, transcriptomics, protein sequences, and bioassay data using modality-specific enc followed by a hierarchical attention-based fusion strategy. This encoder is capable of aligning intra- and inter-modal representations while retaining high-level semantic features critical for interaction prediction. We introduce a robust training strategy named Adaptive Curriculum-...