通过多模态融合与自适应机制提升药物靶点相互作用预测精度
GRAM-DTI: adaptive multimodal representation learning for drug target interaction prediction
- 融合分子与蛋白质的四种模态信息，实现更高阶语义对齐
- 在四个数据集上超越现有最佳模型，提升显著且稳定
- 适合药物发现领域研究者，尤其关注多模态学习的应用
药物靶点相互作用（DTI）预测是计算药物发现的核心，支持理性设计、药物重定位和机制解析。尽管深度学习已推动DTI建模发展，但现有方法主要依赖SMILES蛋白对，未能充分利用小分子和蛋白质的丰富多模态信息。本文提出GRAM-DTI，一种预训练框架，将分子与蛋白质的多模态输入整合为统一表示。该框架将基于体积的对比学习扩展至四种模态，捕捉超越传统成对方法的高阶语义对齐。为应对模态信息量差异，提出自适应模态丢弃机制，动态调节各模态在预训练中的贡献。此外，当可用时，引入IC50活性测量作为弱监督信号，使表示更贴近生物意义的相互作用强度。在四个公开数据集上的实验表明，GRAM-DTI始终优于现有最先进基线。结果凸显了高阶多模态对齐、自适应模态利用和辅助监督对鲁棒且可泛化DTI预测的重要价值。
原文摘要 · Abstract (English)
Drug target interaction (DTI) prediction is a cornerstone of computational drug discovery, enabling rational design, repurposing, and mechanistic insights. While deep learning has advanced DTI modeling, existing approaches primarily rely on SMILES protein pairs and fail to exploit the rich multimodal information available for small molecules and proteins. We introduce GRAMDTI, a pretraining framework that integrates multimodal molecular and protein inputs into unified representations. GRAMDTI extends volume based contrastive learning to four modalities, capturing higher-order semantic alignment beyond conventional pairwise approaches. To handle modality informativeness, we propose adaptive modality dropout, dynamically regulating each modality's contribution during pre-training. Additionally, IC50 activity measurements, when available, are incorporated as weak supervision to ground representations in biologically meaningful interaction strengths. Experiments on four publicly available datasets demonstrate that GRAMDTI consistently outperforms state of the art baselines. Our results highlight the benefits of higher order multimodal alignment, adaptive modality utilization, and auxiliary supervision for robust and generalizable DTI prediction.
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