AI-enabled discovery of small molecules targeting complementary pathways for hair follicle rejuvenation

Abstract

Hair thinning arises from multi-faceted dysfunction within the hair follicle, driven by both intrinsic cellular pathways and pathways responding to extrinsic hormonal and microenvironmental cues. Here, we present an AI-enabled discovery framework to discover small molecules that promote hair follicle rejuvenation. This framework integrates graph neural networks trained on phenotypic screening data with structure-based virtual screening to prioritize compounds that modulate complementary biological pathways. Through AI-enabled screening, hit-to-lead optimization, and medicinal chemistry, we identified four compounds that increase follicle dermal papilla cell viability, stabilize hypoxia signaling by inhibiting prolyl hydroxylase domain protein 2 (PHD2), and suppress androgen-mediated follicular miniaturization by inhibiting 5-reductases (5-ARs). RNA sequencing analyses confirmed pathway engagement, and functional validation across primary cells and a 3D hair follicle organoid model demonstrated high activity and cellular specificity. The lead compounds were incorporated into a water-based formulation, where they demonstrated robust solubility and combinatorial efficacy to increase sprouting length of follicle organoids. These results establish an AI-enabled platform for discovering multi-pathway modulators of hair follicle rejuvenation.