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Available4.9
26 reviewsInvestigating cell morphology changes after perturbations using highthroughput image-based profiling is increasingly important for phenotypicdrug discovery, including predicting mechanisms of action (MOA) and compound bioactivity. The vast space of chemical and genetic perturbationsmakes it impractical to explore all possibilities using conventional methods.Here we propose MorphDiff, a transcriptome-guided latent diffusion modelthat simulates high-fidelity cell morphological responses to perturbations. Wedemonstrate MorphDiff’s effectiveness on three large-scale datasets, includingtwo drug perturbation and one genetic perturbation dataset, covering thousands of perturbations. Extensive benchmarking shows MorphDiff accuratelypredicts cell morphological changes under unseen perturbations. Additionally, MorphDiff enhances MOA retrieval, achieving an accuracy comparable toground-truth morphology and outperforming baseline methods by 16.9% and8.0%, respectively. This work highlights MorphDiff’s potential to acceleratephenotypic screening and improve MOA identification, making it a powerfultool in drug discovery.