A Machine Learning-Guided Approach for Identifying Potential HCAR1 Antagonists in Lactate-Driven Cancers

High Resolution Image Download MS PowerPoint Slide GPR81 (HCAR1) is a lactate-sensing G protein-coupled receptor (GPCR) involved in tumor progression, immune evasion, and therapeutic resistance across various cancers. Despite their clinical relevance and druggable nature, selective HCAR1 antagonists have yet to be identified. This study aimed to construct a statistically significant Support Vector Machine (SVM) model for binary classification (agonists versus antagonists) of HCAR1’s potential ligands and the prioritization of molecular substructures driving antagonism and receptor selectivity. 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). Their ligands were encoded using physicochemical descriptors, 2048-bit ECFP4 fingerprints, and ΔAffinity scores from molecular docking to active and inactive receptor conformations. The data set was split 80/20 for training and testing, respectively, with hyperparameters ( C, γ) being optimized via 5-fold cross-validation. SHAP analysis was performed for feature interpretation. The final SVM model achieved a test set accuracy of 79.3%, with a sensitivity of 69.2% and specificity of 87.5%. The ROC analysis yielded an AUC of 0.94, while bootstrapping confirmed robust performance with a mean AUC of 0.874 and a 95% confidence interval [0.711, 1.000]. SHAP analysis highlighted polar, rigid, and aromatic substructures as selectivity-driving features. We applied the model to screen 3,377 compounds from natural products, synthetic libraries, and FDA-approved drugs, prioritizing potential HCAR1 ligands with antagonist-like features. 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. Rather than representing final hits, these molecules illustrate how structural and electronic features can favor the stabilization of inactive states in HCAR1. 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.