Selvam Subathra, Balaji Priya Dharshini, Sohn Honglae, Madhavan Thirumurthy
Computational Biology Laboratory, Department of Genetic Engineering, School of Bioengineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu 603203, Tamil Nadu, India.
Department of Chemistry, Chosun University, Gwangju 501-759, Republic of Korea.
Pharmaceuticals (Basel). 2024 Dec 15;17(12):1693. doi: 10.3390/ph17121693.
Inflammation serves as a vital response to diverse harmful stimuli like infections, toxins, or tissue injuries, aiding in the elimination of pathogens and tissue repair. However, persistent inflammation can lead to chronic diseases. Peptide therapeutics have gained attention for their specificity in targeting cells, yet their development remains costly and time-consuming. Therefore, small molecules, with their stability, low immunogenicity, and oral bioavailability, have become a focal point for predicting anti-inflammatory small molecules (AISMs). In this study, we introduce a computational method called AISMPred, designed to classify AISMs and non-AISMs. To develop this approach, we constructed a dataset comprising 1750 AISMs and non-AISMs, each annotated with IC50 values sourced from the PubChem BioAssay database. We computed two distinct types of molecular descriptors using PaDEL and Mordred tools. Subsequently, these descriptors were concatenated to form a hybrid feature set. The SVC-L1 regularization method was implemented for the optimum feature selection to develop robust Machine learning (ML) models. Five different conventional ML classifiers were employed, such as RF, ET, KNN, LR, and Ensemble methods. A total of 15 ML models were developed using 2D, FP, and Hybrid feature sets, with the ET model with hybrid features achieving the highest accuracy of 92% and an AUC of 0.97 on the independent test dataset. This study provides an effective method for screening AISMs, potentially impacting drug discovery and design.
炎症是对多种有害刺激（如感染、毒素或组织损伤）的重要反应，有助于消除病原体和组织修复。然而，持续性炎症会导致慢性疾病。肽疗法因其靶向细胞的特异性而受到关注，但其开发仍然成本高昂且耗时。因此，小分子因其稳定性、低免疫原性和口服生物利用度，已成为预测抗炎小分子（AISM）的焦点。在本研究中，我们引入了一种名为AISMPred的计算方法，用于对AISM和非AISM进行分类。为了开发这种方法，我们构建了一个包含1750个AISM和非AISM的数据集，每个数据集都标注了来自PubChem生物测定数据库的IC50值。我们使用PaDEL和Mordred工具计算了两种不同类型的分子描述符。随后，将这些描述符连接起来形成一个混合特征集。采用SVC-L1正则化方法进行最优特征选择，以开发强大的机器学习（ML）模型。使用了五种不同的传统ML分类器，如RF、ET、KNN、LR和集成方法。使用二维、指纹和混合特征集共开发了15个ML模型，其中具有混合特征的ET模型在独立测试数据集上达到了92%的最高准确率和0.97的AUC。本研究为筛选AISM提供了一种有效方法，可能会影响药物发现和设计。
