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A Machine Learning-Guided Approach for Identifying Potential HCAR1 Antagonists in Lactate-Driven Cancers
Letícia Vivas Carvalho1, Núbia Seyffert1, Roberto Meyer1
1Institute of Health Sciences (ICS), Federal University of Bahia (UFBA), Av. Reitor Miguel Calmon, S/N Canela, Salvador, Bahia 40231-300, Brazil.
ACS Omega
|February 23, 2026
Summary
This study developed a machine learning model to identify potential HCAR1 antagonists, crucial for targeting lactate-driven tumors. The model prioritizes compounds with specific structural features, offering insights for designing new cancer therapies.
Area of Science:
- Pharmacology
- Computational Chemistry
- Machine Learning
Background:
- GPR81 (HCAR1), a lactate-sensing receptor, is implicated in cancer progression and therapeutic resistance.
- Selective antagonists for HCAR1 are currently lacking, representing a significant unmet need in oncology.
Purpose of the Study:
- To develop a statistically validated Support Vector Machine (SVM) model for classifying HCAR1 ligands as agonists or antagonists.
- To identify key molecular substructures driving HCAR1 antagonism and receptor selectivity.
- To screen compound libraries for potential HCAR1 antagonists.
Main Methods:
- Training an SVM model on 144 known HCAR1 ligands using physicochemical descriptors and ECFP4 fingerprints.
- Employing molecular docking against active and inactive receptor conformations to calculate ΔAffinity scores.
- Utilizing SHAP analysis for feature interpretation and model explainability.
- Screening 3,377 compounds from diverse libraries.
Main Results:
- The SVM model achieved 79.3% accuracy on the test set, with an AUC of 0.94, indicating robust predictive performance.
- SHAP analysis identified polar, rigid, and aromatic substructures as critical for HCAR1 antagonism and selectivity.
- Screening identified Ketanserin, Cryptopyranmoscatone A1 diacetate, and Cefuroxime as potential HCAR1 antagonists, with two being FDA-approved drugs.
Conclusions:
- The study presents a validated computational framework for discovering selective HCAR1 antagonists.
- The identified structural determinants provide valuable insights for fragment-based and bioisosteric design of novel cancer therapeutics.
- This approach can accelerate the development of drugs targeting lactate-driven cancers.
