Large-scale experimental validation of phenotype-guided generative AI for de novo drug discovery

Abstract

Generative AI is increasingly used to design drugs with desired physicochemical and biological properties. We have previously used the cGAN algorithm trained on cell painting data to generate molecules targeting 10 molecular cancer targets. Here, we provide experimental evidence of the cGAN performance in designing chemical compounds with desired biological activity. We chose a subset of the AI-generated structures for chemical synthesis and tested their activity in a cell painting and a transcriptomic assay. In summary, 88% of the compounds had a pronounced impact on overall cellular morphology, and 37% of the compounds significantly affected transcriptomic pathways associated with the intended target. In a cellular activity assay for an exemplary target, TP53, 6 out of 9 compounds showed potential to modulate TP53 activity. Overall, we show the value of conditioning a generative model on phenotypic readouts for hit discovery of new small molecule drug candidates and pave the way toward AI-guided chemical safety by design.