Devil in the Tail: A Multi-Modal Framework for Drug-Drug Interaction Prediction in Long Tail Distinction

2024/10/16 by Liangwei Nathan Zheng, Chang George Dong, Zheng, Liangwei Nathan +9 · 1 citation
Computer Science · #Computational Drug Discovery Methods #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2410.12249

openalex publication_date 2024/10/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Drug-drug interaction (DDI) identification is a crucial aspect of pharmacology research. There are many DDI types (hundreds), and they are not evenly distributed with equal chance to occur. Some of the rarely occurred DDI types are often high risk and could be life-critical if overlooked, exemplifying the long-tailed distribution problem. Existing models falter against this distribution challenge and overlook the multi-faceted nature of drugs in DDI prediction. In this paper, a novel multi-modal deep learning-based framework, namely TFDM, is introduced to leverage multiple properties of a drug to achieve DDI classification. The proposed framework fuses multimodal features of drugs, including graph-based, molecular structure, Target and Enzyme, for DDI identification. To tackle the challenge posed by the distribution skewness across categories, a novel loss function called Tailed Focal Loss is introduced, aimed at further enhancing the model performance and address gradient vanishing problem of focal loss in extremely long-tailed dataset. Intensive experiments over 4 challenging long-tailed dataset demonstrate that the TFMD outperforms the most recent SOTA methods in long-tailed DDI classification tasks. The source code is released to reproduce our experiment results: https://github.com/IcurasLW/TFMD_Longtailed_DDI.git

Cited by

• Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate

Related

• AttentionDDI: Siamese Attention-based Deep Learning method for drug-drug\n interaction predictions
• Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding\n Approach
• LLM³-DTI: A Large Language Model and Multi-modal data co-powered framework for Drug-Target Interaction prediction
• M3ST-DTI: A multi-task learning model for drug-target interactions based on multi-modal features and multi-stage alignment
• RGDA-DDI: Residual graph attention network and dual-attention based framework for drug-drug interaction prediction