CP-Agent: 化学扰动下细胞形态学轮廓的上下文感知多模态推理
CP-Agent: Context-Aware Multimodal Reasoning for Cellular Morphological Profiling under Chemical Perturbations
AI总结：
提出CP-Agent，一种基于上下文感知对齐模块CP-CLIP的多模态大语言模型，用于生成药物扰动下细胞形态变化的可解释机制性解释，实现高精度处理与机制区分（最大F1分数0.896），并整合工具使用与推理生成结构化报告以加速药物发现。
摘要：
Cell Painting结合多重荧光染色、高内涵成像和定量分析，生成高维表型读数，以支持多种下游任务，如作用机制（MoA）推断、毒性预测和药物-疾病图谱构建。然而，现有工作流程缓慢、昂贵且难以解释。药物筛选建模方法主要侧重于分子表示学习，而忽略了实际实验上下文（例如细胞系、给药方案等），限制了泛化性和MoA分辨率。我们引入了CP-Agent，一种智能多模态大语言模型（MLLM），能够为药物扰动下的细胞形态变化生成与机制相关、人类可解释的理由。其核心是CP-Agent利用上下文感知对齐模块CP-CLIP，该模块联合嵌入高内涵图像和实验元数据，以实现稳健的处理和MoA区分（达到最大F1分数0.896）。通过将CP-CLIP输出与智能工具使用和推理相结合，CP-Agent将理由编译成结构化报告，以指导实验设计和假设优化。这些能力凸显了CP-Agent通过实现更可解释、可扩展和上下文感知的表型筛选来加速药物发现的潜力——简化药物发现中假设生成的迭代循环。
英文摘要：
Cell Painting combines multiplexed fluorescent staining, high-content imaging, and quantitative analysis to generate high-dimensional phenotypic readouts to support diverse downstream tasks such as mechanism-of-action (MoA) inference, toxicity prediction, and construction of drug-disease atlases. However, existing workflows are slow, costly and difficult to interpret. Approaches for drug screening modeling predominantly focus on molecular representation learning, while neglecting actual experimental context (e.g., cell line, dosing schedule, etc.), limiting generalization and MoA resolution. We introduce CP-Agent, an agentic multimodal large language model (MLLM) capable of generating mechanism-relevant, human-interpretable rationales for cell morphological changes under drug perturbations. At its core, CP-Agent leverages a context-aware alignment module, CP-CLIP, that jointly embeds high-content images and experimental metadata to enable robust treatment and MoA discrimination (achieving a maximum F1-score of 0.896). By integrating CP-CLIP outputs with agentic tool usage and reasoning, CP-Agent compiles rationales into a structured report to guide experimental design and hypothesis refinement. These capabilities highlight CP-Agent's potential to accelerate drug discovery by enabling more interpretable, scalable, and context-aware phenotypic screening -- streamlining iterative cycles of hypothesis generation in drug discovery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
