CellPrism: A Visual Analytics System for Exploring AI-Driven Virtual Cells in Drug Discovery

2026-08-03

AI Summary

This work addresses the challenge of efficiently exploring and interpreting the high-dimensional combinatorial space of gene perturbations generated by AI-based virtual cell models and their complex transcriptional responses across diverse cell types. To this end, we propose a visual analytics system that, for the first time, integrates clustered overviews, compact glyph-based encodings, and coordinated multi-view interactions to enable systematic comparison and interpretable exploration of perturbation strategies. By incorporating AI-generated predictions and validating through real-world case studies and expert interviews, we demonstrate that our approach substantially enhances researchers’ understanding of perturbation effects and improves decision-making efficiency in drug discovery, effectively bridging the cognitive gap between computational models and biomedical experts.

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.

Problem

• gene perturbation
• virtual cells
• drug discovery
• high-dimensional data
• visual analytics

Innovation

• visual analytics
• virtual cell
• gene perturbation
• drug discovery
• glyph-based visualization