Department of Quantum Chemistry, Faculty of Chemistry, Adam Mickiewicz University in Poznan, Uniwersytetu Poznanskiego 8, 61-614 Poznan, Poland.
Department of Bioactive Products, Faculty of Chemistry, Adam Mickiewicz University in Poznan, Uniwersytetu Poznanskiego 8, 61-614 Poznan, Poland.
Int J Mol Sci. 2023 Jul 12;24(14):11349. doi: 10.3390/ijms241411349.
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. Finally, the experimental evaluation of the computational results was undertaken in vitro. The proposed methodology resulted in the creation of a library of new indole-based compounds with assigned cytoprotective activity. The other outcome of this study was the development of a validated predictive model capable of estimating cytoprotective activity to a certain extent using molecular structure as input, supported by experimental confirmation.
人工智能（AI）目前得到了广泛的探索，它为增强 QSAR 研究中的经典方法提供了机会。本研究的目的是研究在氧化应激条件下吲哚类结构的细胞保护活性参数，最终目标是开发能够预测细胞保护活性并生成新型吲哚类化合物的 AI 模型。我们提出了一种新的 AI 系统，该系统能够根据一些已知的细胞保护活性建议新的化学结构。构建了基于随机森林、决策树、支持向量机、K-最近邻和多元线性回归等算法的细胞保护活性预测模型，并使用最佳（基于质量测量）模型进行预测。最后，在体外进行了计算结果的实验评估。所提出的方法产生了一个新的具有指定细胞保护活性的吲哚类化合物库。本研究的另一个结果是开发了一个经过验证的预测模型，该模型能够在一定程度上使用分子结构作为输入来估计细胞保护活性，得到了实验的证实。
