Learning Molecular Representations from Cellular Phenotypes with Structure Preservation
Phenotypic drug discovery enables the discovery of functional relationships between molecular structures and cellular responses. However, existing multimodal representation learning methods often optimize cross-modal alignment without considering the intrinsic organization of chemical space, resulting in distorted molecular representations and loss of structural information. We propose \textbf{PhenMol}, a structure-preserving framework for phenotype-aware molecular representation learning. PhenM
Record details
Published: 3 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Safety & alignment · Healthcare · Biotech
Retrieved: 5 August 2026
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ethics.ai (3 August 2026), “Learning Molecular Representations from Cellular Phenotypes with Structure Preservation,” evidence record 16582, https://ethics.ai/record/16582 (originally published by arXiv cs.LG).
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