BiGMINT：生物学引导的分层多模态集成用于药物发现中多化合物活性建模
BiGMINT: Biologically-guided Hierarchical Multimodal Integration for Modeling Multiple Compound Activities in Drug Discovery
论文提出的化学蛋白质组学引导的表型聚合与任务感知的跨模态融合机制，可迁移到视觉-组学联合建模场景，为利用生物先验指导图像特征聚合提供可复用范式。
原文依据 chemoproteomics-guided phenotypic aggregation, task-aware cross-modal fusion, and protein-protein interaction priors
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论化合物活性建模对药物发现至关重要，准确的计算机预测可显著减少对昂贵、耗时的靶点特异性实验分析的依赖。传统的化合物活性建模机器学习方法通常依赖以化学蛋白质组学为中心的分子数据或以表型为中心的成像筛选，限制了其捕获互补生物信号的能力。尽管多模态方法展现出前景，但它们往往无法捕捉分子机制与细胞响应之间的相互作用。本文提出BiGMINT，一个生物学引导的多模态框架，分层集成化学蛋白质组学和高内涵成像（HCI）数据，引入化学蛋白质组学引导的表型聚合、任务感知的跨模态融合以及蛋白质-蛋白质相互作用先验来建模活性。在两个大规模内部数据集（来自U2OS和iNeuron的99K和40K化合物-HCI对）上，BiGMINT相比最佳单模态和多模态方法，平均AUCROC分别提升高达10.0%和4.2%，高性能任务覆盖率分别提升高达103%和56%。深入分析揭示了机制性见解，表明这些增益源于模态互补性，且蛋白质-蛋白质先验增强了对挑战性活性的建模。
英文原文摘要 1339 字符
Compound activity modeling is critical for drug discovery, where accurate *in silico* predictions can significantly reduce reliance on expensive, time-consuming target-specific experimental assays. Traditional machine learning approaches for compound activity modeling typically rely on either chemoproteomics-centric molecular data or phenotype-centric imaging screens, limiting their ability to capture complementary biological signals. While multimodal approaches show promise, they often fail to capture the interplay between molecular mechanisms and cellular responses. In this paper, we present **BiGMINT**, a **Bi**ologically **G**uided **M**ultimodal framework that hierarchically **INT**egrates chemoproteomic and high-content imaging (HCI) data, introducing chemoproteomics-guided phenotypic aggregation, task-aware cross-modal fusion, and protein-protein interaction priors for modeling activities. On two large-scale in-house datasets, with 99K and 40K compound-HCI pairs from U2OS and iNeuron, **BiGMINT** improves mean AUCROC by up to 10.0% and 4.2%, and high-performing task coverage by up to 103% and 56% over best unimodal and multimodal methods. Thorough analysis revealed mechanistic insights, showing these gains stem from modality complementarity, and protein-protein priors enhance modeling of challenging activities.