CellPrism: A Visual Analytics System for Exploring AI-Driven Virtual Cells in Drug Discovery
2026-08-03 • Human-Computer Interaction
Human-Computer Interaction
AI summaryⓘ
The authors created CellPrism, a tool that helps scientists understand how changing certain genes affects a whole cell's behavior using computer models. These models predict gene activity when genes are altered, which can speed up drug research without needing as many lab experiments. CellPrism uses visual summaries and special icons to show complex gene responses across different cell types, making it easier to compare and explore these changes. The authors showed that this visual approach helps experts make better decisions in drug discovery.
Gene perturbationGene expressionVirtual cell modelsVisual analyticsDrug discoveryClusteringGlyph-based visualizationHigh-dimensional dataIn silico modeling
Authors
Chuhan Shi, Zijian Guo, Zelin Zang, Chengbo Zheng, Ding Ding, Rui Sheng
Abstract
Gene perturbation analysis plays a critical role in drug discovery by enabling researchers to investigate how interventions on specific genes influence global gene expression patterns within cells. Recent advances in artificial intelligence-driven virtual cell models have made it possible to predict gene expression outcomes for a wide range of perturbation strategies in silico, substantially reducing reliance on costly and time-consuming biological experiments. However, effectively exploring and interpreting the high-dimensional perturbation spaces produced by these models remains challenging because of the combinatorial nature of perturbations and the complex cell-specific gene expression responses they generate. In this work, we present CellPrism, a visual analytics system designed to support the systematic exploration of gene perturbation strategies for drug discovery. Specifically, CellPrism integrates clustering-based overviews to summarize perturbation outcomes, a glyph-based representation to compactly encode gene expression patterns across cell types, and coordinated views that enable fine-grained comparison and interpretation of perturbation effects. We demonstrate the effectiveness of CellPrism through a real-world case study and expert interviews. This work highlights the value of visual analytics in bridging virtual cell modeling with expert-driven decision making in drug discovery.