Ahmed W Eltayeb, Hanif Muhammad Farhan, Siddiqui Muhammad Kamran, Gegbe Brima
Department of Mathematics and Statistics, College of Science, Imam Muhammad Ibn Saud Islamic University (IMSIU), PO Box 90950, Riyadh, Saudi Arabia.
Department of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan.
Sci Rep. 2025 Jul 20;15(1):26356. doi: 10.1038/s41598-025-09878-z.
In this paper, we present a predictive model based on artificial neural network (ANN) to evaluate principal physicochemical properties of a set of anti-inflammatory drugs based on chosen topological indices. The molecular descriptors were calculated from molecular structures and employed as the inputs to the ANN model. Normalization of the feature set was carried out before training to maintain convergence and stability of the model. The ANN exhibited excellent predictive ability based on a [Formula: see text] value of 0.94 and a mean squared error (MSE) of 0.0087 on the test set. The chemical structure data used were mainly retrieved from ChemSpider. The method showcases the promise of machine learning models to facilitate better virtual screening and assist in rational drug design by making accurate predictions of properties.
在本文中，我们提出了一种基于人工神经网络（ANN）的预测模型，以基于所选拓扑指数评估一组抗炎药物的主要物理化学性质。分子描述符由分子结构计算得出，并用作ANN模型的输入。在训练前对特征集进行归一化处理，以保持模型的收敛性和稳定性。基于测试集上0.94的[公式：见正文]值和0.0087的均方误差（MSE），ANN表现出出色的预测能力。所使用的化学结构数据主要从ChemSpider中检索。该方法展示了机器学习模型通过准确预测性质来促进更好的虚拟筛选并协助合理药物设计的前景。
