Artificial Intelligence in Decrypting Cytoprotective Activity under Oxidative Stress from Molecular Structure.

ABSTRACT: Artificial intelligence (AI) is widely explored nowadays, and it gives opportunities to enhance classical approaches in QSAR studies. The aim of this study was to investigate the cytoprotective activity parameter under oxidative stress conditions for indole-based structures, with the ultimate goal of developing AI models capable of predicting cytoprotective activity and generating novel indole-based compounds. We propose a new AI system capable of suggesting new chemical structures based on some known cytoprotective activity. Cytoprotective activity prediction models, employing algorithms such as random forest, decision tree, support vector machines, K-nearest neighbors, and multiple linear regression, were built, and the best (based on quality measurements) was used to make predictions. F

SUBMITTER: Nowak D 

PROVIDER: S-EPMC10379162 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature