A Machine Learning-Guided Approach for Identifying Potential HCAR1 Antagonists in Lactate-Driven Cancers. (PubMed, ACS Omega)
An SVM model was trained on 144 ligands (66 agonists, 78 antagonists), listed in the IUPHAR/BPS Guide to Pharmacology, from 12 structurally related Class A GPCRs (HCAR1, HCAR2, HCAR3, OXER1, GPR35, SUCNR1, P2Y2, MCHR1, OPRD1, AGTR1, ADORA2A, and ADRA1A)...Based on ΔAffinity, off-target scores, and prediction confidence, Ketanserin, Cryptopyranmoscatone A1 diacetate, and Cefuroxime emerged as reference ligands with promising antagonistic potential, two of which are FDA-approved drugs...Overall, this work presents a proof-of-concept framework that integrates conformational docking, machine learning, and substructure interpretation to elucidate the chemical and structural determinants of HCAR1 antagonism. The findings provide fragment-level insights that may guide future bioisosteric and fragment-based design of selective antagonists for lactate-driven tumors.