Environmental contaminants cause 16% of premature deaths worldwide, yet most chemicals in commercial use have never been assessed for their potential to impact human health. Traditional chemical safety evaluation methods that use live animals are informative, but too expensive and slow to assess environmental chemical contributions to disease at scale.

Drug discovery programs routinely screen thousands to millions of compounds in computational and cellular models for their potential to treat disease. What if we used this same technology to assess whether chemicals used in other sectors of society are dangerous? Scientists and government regulators are urgently working to implement high-throughput screening methods to prioritize resource-instensive testing for the most hazardous compounds.

A brute-force approach of testing every compound in all cell types and disease contexts is impractical. Instead, scientists must develop computational approaches that leverage prior knowledge of cell types, cell states, and their relationship to disease to design efficient chemical screens and extrapolate observed perturbations across biological space and scales.