CP-Agent: Context-Aware Multimodal Reasoning for Cellular Morphological Profiling under Chemical Perturbations
The article introduces CP-Agent, a multimodal large language model designed for cellular morphological profiling under chemical perturbations. This model enhances drug discovery by providing interpretable rationales for cell morphological changes, leveraging a context-aware alignment module. CP-Agent aims to streamline the drug screening process by integrating high-content imaging with experimental metadata.
- ▪CP-Agent combines high-content imaging and experimental metadata to improve drug discovery.
- ▪The model achieves a maximum F1-score of 0.896 for treatment and mechanism-of-action discrimination.
- ▪CP-Agent generates structured reports to assist in experimental design and hypothesis refinement.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2606.03435 |
| Publication time | Wed, 03 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-03T04:11:55.408Z |
| Last seen | 2026-06-03T04:11:55.408Z |
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| Summary source text | contentText |
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| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2606.03435 (cs) [Submitted on 2 Jun 2026] Title:CP-Agent: Context-Aware Multimodal Reasoning for Cellular Morphological Profiling under Chemical Perturbations Authors:Yuxin Zhang, Yiyao Li, Ping Shu Ho, Simon See, Zhenqin Wu, Kevin Tsia View a PDF of the paper titled CP-Agent: Context-Aware Multimodal Reasoning for Cellular Morphological Profiling under Chemical Perturbations, by Yuxin Zhang and 5 other authors View PDF HTML (experimental) Abstract: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.
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