LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.
CINBIO, Department of Computer Science, ESEI-Escuela Superior de Ingeniería Informática, Universidade de Vigo, 32004 Ourense, Spain.
Molecules. 2023 Jan 25;28(3):1182. doi: 10.3390/molecules28031182.
Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug-enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP). Validation of the final best model found was carried out by internal cross-validation statistics and other relevant diagnostic statistical parameters. The overall accuracy of the derived model was found to be higher than 96%. Finally, to maximize the diffusion of this model, a public and accessible tool has been developed to allow users to perform their own predictions. The developed web-based tool is public accessible and can be downloaded as free open-source software.
开发能够预测药物与酶之间相互作用的模型是计算生物学的主要目标，因为这些模型可用于预测新的活性药物以及已知药物在未经测试的靶标上的相互作用。通过编译一个包含大量药物-酶对（62524 对）的数据集，我们认识到这是一个独特的机会，可以尝试构建一种新的多靶机器学习（MTML）定量构效关系（QSAR）模型，以探究不同药物和酶靶标之间的相互作用。为此，本文提出了一种基于拓扑药物特征与人工神经网络（ANN）多层感知器（MLP）的 MTML-QSAR 模型。通过内部交叉验证统计和其他相关诊断统计参数对最终最佳模型进行了验证。所得到的模型的整体准确性被发现高于 96%。最后，为了最大限度地推广该模型，我们开发了一个公共且可访问的工具，允许用户进行自己的预测。开发的基于网络的工具是公共可访问的，可以作为免费的开源软件下载。
