ArticleACS omega2026
A Machine Learning-Guided Approach for Identifying Potential HCAR1 Antagonists in Lactate-Driven Cancers.
Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 (
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.