CSIL: A Chirality-Structural Interaction Learning Framework for Chiral Drug-Drug Interaction Prediction

Accurate prediction of drug-drug interactions (DDIs) is essential for drug safety and healthcare research. Many drugs are chiral, and their molecules can exist in non-superimposable mirror-image forms. These forms share the same molecular formula and atomic connectivity but differ in chiral configuration. When two chiral drugs interact, different configuration combinations may alter biological recognition and lead to different interaction outcomes. This motivates the study of Chiral Drug-Drug Interaction (CDDI) prediction. However, many existing DDI methods focus on molecular connectivity and substructures without explicitly considering chirality, which may limit their ability to capture differences caused by chiral configuration and accurately predict CDDIs. Moreover, their substructure-based explanations do not explicitly account for chirality-related information, making it difficult to explain how chiral configuration affects CDDIs. To overcome these limitations, we propose a Chirality-Structural Interaction Learning (CSIL) framework for CDDI prediction. CSIL integrates hierarchical motif representations with configuration-sensitive chirality representations and employs chirality-motif collaborative cross-attention to model their cross-drug dependencies. It further highlights interaction-relevant motifs and configuration-sensitive cues associated with CDDI predictions, providing fine-grained attributions from both structural and chirality-related perspectives. Extensive experiments on a CDDI dataset curated from DrugBank demonstrate that CSIL achieves the best overall rank against representative baselines across four settings. The source code and datasets are submitted as Supplementary Materials for Reproducibility.