QSAR Modeling of Analgesic Cannabinoids via Dual Feature Selection and Support Vector Machines: A Cheminformatics Case Study

Abstract

This study aimed to develop an accessible Quantitative Structure–Activity Relationship model based on Machine Learning techniques for a set of analgesic cannabinoid compounds. It represents a cheminformatics contribution to the promising field of endocannabinoid system modulation. Three-dimensional molecular structures and biological activity data were retrieved from PubChem. Molecular descriptors were calculated using Dragon7 software and subjected to variable selection through two complementary feature selection strategies: Wrapper and Filter methods. The final predictive model was constructed using Support Vector Machines and validated through both K-fold cross-validation and leave-one-out approaches. A robust and predictive model comprising 29 molecular descriptors was obtained, enabling the prediction of novel thiophenyl-acetamide analogs. The combined use of two feature selection techniques proved effective in capturing relevant molecular information and enhancing model performance. This model offers valuable support for the synthetic optimization and design of more potent cannabinoid-based analgesics.