General structure-activity relationship models for the inhibitors of Adenosine receptors: A machine learning approach.

Abstract

Adenosine receptors (A₁, A_2a, A_2b, A₃) play critical roles in cellular signaling and are implicated in various physiological and pathological processes, including inflammations and cancer. The main aim of this research was to investigate structure-activity relationships (SAR) to derive models that describe the selectivity and activity of inhibitors targeting Adenosine receptors. Structural information for 16,312 inhibitors was collected from BindingDB and analyzed using machine learning methods. 450 molecular descriptors were calculated for each molecule and compounds were classified based on their activity levels and therapeutic targets. The variable importance in projection (VIP) algorithm identified key discriminating features. Classification models were built using supervised Kohonen networks (SKN) and counter-propagation artificial neural networks (CPANN) algorithms. Model validity was assessed via cross-validation, applicability domain analysis, and test sets. These models were then used to screen a random subset of 2 million molecules from the ZINC database. Three descriptors-hydrophilic factor (Hy), ratio of multiple path count over path count (PCR), and asphericity (ASP)-were identified as critical for discriminating active and inactive inhibitors. SKN models exhibited high sensitivity (0.88-0.99) and yielded an average area under the curve (AUC) of 0.922 for virtual screening. This study aimed to enhance the development of highly selective Adenosine receptor ligands for diverse therapeutic applications by identifying critical molecular features specific to each isoform.