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This gap arises from the lack of robust computational methods for unbiased cavity identification, cavity-specific ligand design, synthesis, and validation across GPCR topology. Here, we developed Gcoupler, an AI-driven generalized computational toolkit that leverages an integrative approach combining de novo ligand design, statistical methods, Graph Neural Networks, and bioactivity-based ligand prioritization for rationally predicting high-affinity ligands. Using Gcoupler, we interrogated intracellular metabolites that target and regulate the GPCR\u2013G\u03b1 interface (Ste2p\u2013Gpa1p), affecting pheromone-induced programmed cell death in yeast. Our computational analysis, complemented by experimental validations, including genetic screening, multi-omics, site-directed mutagenesis, biochemical assays, and physiological readouts, identified endogenous hydrophobic metabolites, notably sterols, as direct intracellular allosteric modulators of Ste2p. Molecular simulations coupled with biochemical signaling assessment in site-directed Ste2p mutants further confirmed that metabolites binding to GPCR\u2013G\u03b1 obstruct downstream signaling, possibly via a cohesive effect. Finally, by utilizing isoproterenol-induced, GPCR-mediated human and neonatal rat cardiac hypertrophy models, we observed that elevated metabolite levels attenuate hypertrophic response, reinforcing the evolutionary relevance of this mechanism.<\/jats:p>","DOI":"10.7554\/elife.106397","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T10:30:49Z","timestamp":1748601049000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.106397","source":"Crossref","is-referenced-by-count":1,"title":["Deep learning reveals endogenous sterols as allosteric modulators of the GPCR\u2013G\u03b1 interface"],"prefix":"10.7554","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1375-2223","authenticated-orcid":true,"given":"Sanjay Kumar","family":"Mohanty","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03vfp4g33","id-type":"ROR","asserted-by":"publisher"}],"name":"Department of Computational Biology, Indraprastha Institute of Information 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