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

IF 3.9 2区 化学 Q2 CHEMISTRY, APPLIED
M Janbozorgi, S Kaveh, M S Neiband, A Mani-Varnosfaderani
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引用次数: 0

Abstract

Adenosine receptors (A1, A2a, A2b, 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.

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来源期刊
Molecular Diversity
Molecular Diversity 化学-化学综合
CiteScore
7.30
自引率
7.90%
发文量
219
审稿时长
2.7 months
期刊介绍: Molecular Diversity is a new publication forum for the rapid publication of refereed papers dedicated to describing the development, application and theory of molecular diversity and combinatorial chemistry in basic and applied research and drug discovery. The journal publishes both short and full papers, perspectives, news and reviews dealing with all aspects of the generation of molecular diversity, application of diversity for screening against alternative targets of all types (biological, biophysical, technological), analysis of results obtained and their application in various scientific disciplines/approaches including: combinatorial chemistry and parallel synthesis; small molecule libraries; microwave synthesis; flow synthesis; fluorous synthesis; diversity oriented synthesis (DOS); nanoreactors; click chemistry; multiplex technologies; fragment- and ligand-based design; structure/function/SAR; computational chemistry and molecular design; chemoinformatics; screening techniques and screening interfaces; analytical and purification methods; robotics, automation and miniaturization; targeted libraries; display libraries; peptides and peptoids; proteins; oligonucleotides; carbohydrates; natural diversity; new methods of library formulation and deconvolution; directed evolution, origin of life and recombination; search techniques, landscapes, random chemistry and more;
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