Predicting compound activity from phenotypic profiles and chemical structures

Recent advances in deep learning enable using chemical structures and phenotypic profiles to accurately predict assay results for compounds virtually, reducing the time and cost of screens in the drug-discovery process. We evaluate the relative strength of three high-throughput data sources—chemical structures, images (Cell Painting), and gene-expression profiles (L1000)—to predict compound activity using a sparse historical collection of 16,186 compounds tested in 314 assays for a total of 679,819 readouts. All three data modalities can predict compound activity with high accuracy in 7-8% of assays tested; replacing million-compound physical screens with computationally prioritized smaller screens throughout the pharmaceutical industry could yield major savings. Furthermore, the three profiling modalities are complementary, and in combination they can predict 18% of assays with high accuracy, and up to 59% if lower accuracy is acceptable for some screening projects. Our study shows that, for many assays, predicting compound activity from phenotypic profiles and chemical structures could accelerate the early stages of the drug-discovery process. ### Competing Interest Statement The authors have declared no competing interest.