Large scale compound selection guided by cell painting reveals activity cliffs and functional relationships

作者：Maxime Sanchez, Nicolas Bourriez, Ihab Bendidi, Ethan Cohen, Ivan Svatko, Elaine Del Nery, Hamza Tajmouati, Guillaume Bollot, Laurence Calzone, Auguste Genovesio · 发表于：Communications Biology · 年份：2026 · DOI：10.1038/s42003-025-09500-y · 被引用次数：3 · 研究领域：Cell Image Analysis Techniques、Computational Drug Discovery Methods、Advanced Fluorescence Microscopy Techniques

Traditional structure-based pre-screen compound selection relies on the assumption that chemical similarity implies similar biological activity. This paradigm narrows the exploration of chemical space and often fails to account for functional convergence, where structurally diverse compounds act through distinct targets to produce similar phenotypic effects. As a result, compounds with therapeutic potential may be overlooked. To overcome this constraint, we introduce a training-free, transfer learning-based method for large scale compound preselection that leverages deep phenotypic profiling of human cells. Notably, this enables robust pairwise comparison of phenotypic signatures across any source of the entire JUMP-CP, the largest publicly available cell painting dataset (112,480 compounds), preserving biological signals while mitigating batch effects. Validated across 65 high-throughput assays-including in vitro and in cellulo systems-our method provides efficient pre-screen enrichment of biologically active compounds, bypassing the blind spots of structure-centric approaches. Interestingly, because it is large scale, it also allows for a comprehensive analysis of structure-phenotypic activity relationships, revealing potentially thousands of compound activity cliffs, where minimal chemical changes in structure may result in profound phenotypic shifts. We show that these cliffs capture subtle, atom-level determinants of bioactivity that cannot be accessed by structure-based...