从细胞反应到药理学领域：多模态零样本药物表示学习
From Cellular Responses to Pharmacological Domains: Multimodal Zero-Shot Drug Representation Learning
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中文总结 AI 辅助
研究针对多模态药物发现中存在的问题，提出PMRD框架，通过分离机制因素、构建共识反应域、增强机制候选、动态加权及结合互补表示等方法，实现多模态零样本药物性质预测，提升预测效果并减少化合物间冲突。
中文摘要 AI 辅助
多模态药物发现通过纳入基因表达和细胞形态等细胞反应，实现超越化学结构的药物表示学习。然而，直接融合和实例级对比对齐可能会将与机制相关的信号与模态特定噪声混合，错误地分离结构不同但生物学相关的化合物。我们引入了PMRD，一种用于多模态零样本药物性质预测的药理学反应域引导框架。PMRD将机制一致的因素与模态特定信息分离，构建跨三种模态的共识反应域。机制候选增强识别局部稳定因素，检索几何归因根据更新是否保留药物间距离动态重新加权对齐和增强目标，反馈抑制与机制判别检索冲突的训练信号。PMRD还通过可靠性感知多视图检索结合互补表示。公共数据集实验显示零样本性质预测得到改善，药物邻域更具生物学一致性。硬负分析表明结构不同但反应相关的化合物间冲突减少。这些结果支持PMRD作为机制感知多模态药物表示学习的有效框架。
英文摘要
Multimodal drug discovery enables drug representation learning beyond chemical structure by incorporating cellular responses such as gene expression and cell morphology. However, direct fusion and instance-level contrastive alignment may mix mechanism-related signals with modality-specific noise and incorrectly separate structurally dissimilar but biologically related compounds. This limitation can obscure transferable mechanism patterns required for predicting the properties of unseen compounds. We introduce PMRD, a pharmacological response domain-guided framework for multimodal zero-shot drug property prediction. PMRD separates mechanism-consistent factors from modality-specific information and constructs a consensus response domain across three modalities. Mechanism candidate augmentation identifies locally stable factors, while retrieval-geometry attribution dynamically reweights the alignment and augmentation objectives according to whether their updates preserve inter-drug discriminability.This feedback suppresses training signals that conflict with mechanism-discriminative retrieval. PMRD further combines complementary representations through reliability-aware multiview retrieval. Experiments on public datasets show improved zero-shot property prediction and more biologically coherent drug neighborhoods. Hard-negative analysis further indicates fewer conflicts between structurally dissimilar but response-related compounds. These results support PMRD as an effective framework for mechanism-aware multimodal drug representation learning.\footnote{The code will be released upon publication.}
