Conceptual DFT, machine learning and molecular docking as tools for predicting LD 50 toxicity of organothiophosphates.

Autor: Rangel-Peña UJ; Area Académica de Química, Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Hidalgo, Km. 4.5 Carretera Pachuca-Tulancingo, Ciudad del Conocimiento, C.P. 42184, Mineral de La Reforma, Hidalgo, México., Zárate-Hernández LA; Area Académica de Química, Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Hidalgo, Km. 4.5 Carretera Pachuca-Tulancingo, Ciudad del Conocimiento, C.P. 42184, Mineral de La Reforma, Hidalgo, México., Camacho-Mendoza RL; Area Académica de Química, Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Hidalgo, Km. 4.5 Carretera Pachuca-Tulancingo, Ciudad del Conocimiento, C.P. 42184, Mineral de La Reforma, Hidalgo, México., Gómez-Castro CZ; Area Académica de Química, Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Hidalgo, Km. 4.5 Carretera Pachuca-Tulancingo, Ciudad del Conocimiento, C.P. 42184, Mineral de La Reforma, Hidalgo, México., González-Montiel S; Area Académica de Química, Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Hidalgo, Km. 4.5 Carretera Pachuca-Tulancingo, Ciudad del Conocimiento, C.P. 42184, Mineral de La Reforma, Hidalgo, México., Pescador-Rojas M; Escuela Superior de Cómputo, Instituto Politécnico Nacional, Mexico, México., Meneses-Viveros A; Departamento de Computación, CINVESTAV-IPN, Av. IPN 2508, Col. San Pedro Zacatenco, Ciudad de Mexico, 07360, México., Cruz-Borbolla J; Area Académica de Química, Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Hidalgo, Km. 4.5 Carretera Pachuca-Tulancingo, Ciudad del Conocimiento, C.P. 42184, Mineral de La Reforma, Hidalgo, México. jcruz@uaeh.edu.mx.
Jazyk: angličtina
Zdroj: Journal of molecular modeling [J Mol Model] 2023 Jun 28; Vol. 29 (7), pp. 217. Date of Electronic Publication: 2023 Jun 28.
DOI: 10.1007/s00894-023-05630-4
Abstrakt: Context: Several 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 (LD 50 ) 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 R 2 values for the training set (R 2 Train ) and R 2 values for the test set (R 2 Test ), around 0.90.
Methods: The 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.
(© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Databáze: MEDLINE
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