Conceptual DFT, machine learning and molecular docking as tools for predicting LD50 toxicity of organothiophosphates.
Conceptual DFT, machine learning and molecular docking as tools for predicting LD50 toxicity of organothiophosphates.
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概念 DFT、机器学习和分子对接作为预测有机硫代磷酸酯 LD50 毒性的工具。
DOI：
10.1007/s00894-023-05630-4
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发表时间：
2023
影响因子：
2.2
通讯作者：
Cruz-Borbolla,Julián
中科院分区：
文献类型：
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作者：
Rangel-Peña,UrielJ;Zárate-Hernández,LuisA;Camacho-Mendoza,RosaL;Gómez-Castro,CarlosZ;González-Montiel,Simplicio;Pescador-Rojas,Miriam;Meneses-Viveros,Amilcar;Cruz-Borbolla,Julián
本文采用随机森林（RF）、LASSO、Ridge、弹性网络（EN）和支持向量机（SVM）等方法对62种有机硫代磷酸酯化合物的毒性（LD_（50））进行了预测。使用RF方法获得A-RF-G1和A-RF-G2模型，产生具有良好性能的统计学显著性参数，如训练集（R2 Train）的R2值和测试集（R2 Test）的R2值所示，方法利用具有6-311 + + G** 的杂化功能基ω B 97 XD对所有硫代磷酸酯进行分子结构优化基组使用各种机器学习算法处理了787个描述符：RF LASSO，Ridge，EN和SVM，以生成预测模型。用Multiwfn、AIMALL和VMD程序计算了这些性质。通过使用AutoDock 4.2和LigPlot +程序进行对接模拟。本文的计算全部在Gaussian 16程序包中进行。
ContextSeveral descriptors from conceptual density functional theory (cDFT) and the quantum theory of atoms in molecules (QTAIM) were utilized in Random Forest (RF), LASSO, Ridge, Elastic Net (EN), and Support Vector Machines (SVM) methods to predict the toxicity (LD50) of sixty-two organothiophosphate compounds. The A-RF-G1 and A-RF-G2 models were obtained using the RF method, yielding statistically significant parameters with good performance, as indicated by R2values for the training set (R2Train) and R2values for the test set (R2Test), around 0.90.MethodsThe molecular structure of all organothiophosphates was optimized via the range-separated hybrid functional ωB97XD with the 6–311 + + G** basis set. Seven hundred and eighty-seven descriptors have been processed using a variety of machine learning algorithms: RF LASSO, Ridge, EN and SVM to generate a predictive model. The properties were obtained with Multiwfn, AIMALL and VMD programs. Docking simulations were performed by using AutoDock 4.2 and LigPlot + programs. All the calculations in this work are carried out in Gaussian 16 program package.