QSAR and scaffold-based optimization of HMGR inhibitors using cheminformatics and machine learning

Atherosclerosis, driven by elevated cholesterol levels, remains a major risk factor for cardiovascular disease. 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGR), the rate-limiting enzyme involved in cholesterol biosynthesis, represents a validated therapeutic target. Statins are an effective class of drugs widely prescribed for HMGR inhibition; however, their prolonged use causes adverse side effects. This highlights the need for novel inhibitors with improved safety and efficacy. In this study, a comprehensive cheminformatics and machine learning approach was applied to identify and optimize potential HMGR inhibitors. A curated dataset from the ChEMBL database was analyzed through physicochemical descriptor profiling, exploratory data analysis, and principal component analysis (PCA). Murcko scaffold extraction revealed that active molecules clustered around complex cyclic frameworks enriched in aromatic and nitrogen-containing motifs. Following this, quantitative structure–activity relationship (QSAR) models were developed using various machine learning algorithms, and it was found that gradient boosting and XGBoost regressors demonstrated the best performance, with a tuned XGBoost achieving a cross-validated R 2 of 0.70. Ligand-based R group enumeration further refined promising cores, enhancing hydrogen bonding, polarity, and multiparameter optimization (MPO) scores. Four scaffolds were successfully optimized, with improved MPO values. Thus, by integrating cheminformatics and machine learning, this study provides a systematic pipeline that highlights promising scaffolds optimizing drug-likeness for the development of novel HMGR inhibitors.