SADRP：面向药物反应预测的结构感知多模态框架
SADRP: A structurally aware multi-modal framework for drug response prediction
SADRP 是一个结构感知的多模态药物反应预测框架，联合建模药物分子结构与细胞系多组学特征。在药物表征上，它引入最短距离路径（SDP）编码与边编码，同时捕捉拓扑关系与键型化学语义；在细胞系表征上，多视图交互编码器整合基因表达与 DNA 甲基化，刻画跨组学协同互补。该框架持续优于现有最先进方法，并能识别潜在治疗候选物与生物标志物，为药物发现和个性化医疗提供机制层面的线索。
来源：PLOS Computational Biology · journals.plos.org
by Ren Qi, Shujia Liu, Tianhong Quan, Tianru Wu, Alexey K. Shaytan, Bin Liu
Motivation
Predicting drug sensitivity across diverse cancer samples is a fundamental challenge in personalized therapy and drug discovery. This task requires comprehensive characterization of both drug and cancer cell line features. Current approaches often fail to fully capture the complex structural characteristics and interactions of drug compounds and struggle to integrate multi-omics features derived from the same sample, limiting their feature representation capacity. Consequently, there is a need for modeling frameworks that can jointly model molecular structural features and multi-modal cellular contexts in a unified manner.
Results
We present SADRP, a structurally aware multi-modal framework for drug response prediction, designed to model complex molecular structures and multi-omics data. For drug representation, SADRP incorporates Shortest Distance Path (SDP) encoding and edge encoding to capture both topological relationships and bond-type chemical semantics. For cell line profiling, a multi-view interactive encoder integrates gene expression and DNA methylation, capturing cross-omics synergistic complementarity. SADRP consistently outperforms state-of-the-art methods, while also identifying potential therapeutic candidates and biomarkers, providing mechanistic insights and supporting applications in drug discovery and personalized medicine.