Department of Chemistry, Tuyserkan Branch, Islamic Azad University, Tuyserkan, Iran.
Drug Design in Silico Lab., Chemistry Faculty, University of Tehran, Tehran, Iran.
Chem Biol Drug Des. 2019 Jun;93(6):1139-1157. doi: 10.1111/cbdd.13511.
A hybrid of artificial intelligence simple and low computational cost QSAR was used. Approximately 90 pyridinylimidazole-based drug candidates with a range of potencies against p38R MAP kinase were investigated. To obtain more flexibility and effective capability of handling and processing information about the real world, in this case, the fuzzy set theory was introduced into the QSAR. An integration of multiple linear regression and artificial neural network with adaptive neuro-fuzzy inference systems (ANFIS) was developed to predict the inhibition activity. The algorithm of ANFIS was applied to identify the suitable variables and then to find the optimal descriptors. The gradient descent with momentum backpropagation ANN was used to establish the nonlinear multivariate relationships between the chemical structural parameters and biological response. A comparison between the result of the proposed linear and nonlinear regression showed the superiority of QSAR modeling by ANFIS-ANN method over the MLR. The results demonstrated that the ANFIS could be applied successfully as a feature selection. The appearance of Diam, Homo, and LogP descriptors in the model showed the importance of the steric, electronic, and thermodynamic interactions between a drug and its target site in the distribution of a compound within a biosystem and its interaction with competing for binding sites.
采用了一种结合了人工智能简单和低计算成本的 QSAR。研究了大约 90 种基于吡啶基咪唑的药物候选物，它们对 p38R MAP 激酶的活性范围不同。为了获得更多的灵活性和有效处理和处理有关真实世界的信息的能力，在这种情况下，模糊集理论被引入到 QSAR 中。为了预测抑制活性，开发了一种将多元线性回归和人工神经网络与自适应神经模糊推理系统（ANFIS）集成的方法。ANFIS 的算法用于识别合适的变量，然后找到最佳描述符。使用具有动量反向传播的梯度下降 ANN 来建立化学结构参数和生物响应之间的非线性多变量关系。将提出的线性和非线性回归的结果进行比较，表明 ANFIS-ANN 方法的 QSAR 建模优于 MLR。结果表明，ANFIS 可以成功地用作特征选择。模型中出现的 Diam、Homo 和 LogP 描述符表明，药物与其靶部位之间的空间、电子和热力学相互作用在化合物在生物系统中的分布以及与竞争结合部位的相互作用中非常重要。
