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Castro G, Cruz-Borbolla J, Galván M, Guevara-García A, Ireta J, Matus MH, Meneses-Viveros A, Ignacio Perea-Ramírez L, Pescador-Rojas M. Hydrodesulfurization of Dibenzothiophene: A Machine Learning Approach. ChemistryOpen 2024:e202400062. [PMID: 38607955 DOI: 10.1002/open.202400062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/18/2024] [Indexed: 04/14/2024] Open
Abstract
The hydrodesulfurization (HDS) process is widely used in the industry to eliminate sulfur compounds from fuels. However, removing dibenzothiophene (DBT) and its derivatives is a challenge. Here, the key aspects that affect the efficiency of catalysts in the HDS of DBT were investigated using machine learning (ML) algorithms. The conversion of DBT and selectivity was estimated by applying Lasso, Ridge, and Random Forest regression techniques. For the estimation of conversion of DBT, Random Forest and Lasso offer adequate predictions. At the same time, regularized regressions have similar outcomes, which are suitable for selectivity estimations. According to the regression coefficient, the structural parameters are essential predictors for selectivity, highlighting the pore size, and slab length. These properties can connect with aspects like the availability of active sites. The insights gained through ML techniques about the HDS catalysts agree with the interpretations of previous experimental reports.
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Affiliation(s)
- Guadalupe Castro
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, Av. Ferrocarril San Rafael Atlixco 186, Col. Leyes de Reforma 1 A Sección, Iztapalapa, C.P. 09310, Ciudad de México, México
| | - Julián Cruz-Borbolla
- Área Académica de Química, Centro de Investigaciones Químicas - Universidad Autónoma del Estado de Hidalgo, Carretera Pachuca-Tulancingo km. 4.5, Ciudad del Conocimiento, C.P. 42184, Mineral de la Reforma, Hidalgo, México
| | - Marcelo Galván
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, Av. Ferrocarril San Rafael Atlixco 186, Col. Leyes de Reforma 1 A Sección, Iztapalapa, C.P. 09310, Ciudad de México, México
| | - Alfredo Guevara-García
- Departamento de Química, CONAHCYT-Universidad Autónoma Metropolitana-Iztapalapa, Av. Ferrocarril San Rafael Atlixco 186, Col. Leyes de Reforma 1 A Sección, Iztapalapa, C.P. 09310, Ciudad de México, México
| | - Joel Ireta
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, Av. Ferrocarril San Rafael Atlixco 186, Col. Leyes de Reforma 1 A Sección, Iztapalapa, C.P. 09310, Ciudad de México, México
| | - Myrna H Matus
- Instituto de Química Aplicada, Universidad Veracruzana, Av. Luis Castelazo Ayala s/n, Col. Industrial-Ánimas, A.P. 575, Xalapa, Ver., México
| | - Amilcar Meneses-Viveros
- Departamento de Computación, CINVESTAV-IPN, Av. IPN 2508, Col. San Pedro Zacatenco, C.P. 07360, Ciudad de Mexico, México
| | - Luis Ignacio Perea-Ramírez
- Instituto de Química Aplicada, Universidad Veracruzana, Av. Luis Castelazo Ayala s/n, Col. Industrial-Ánimas, A.P. 575, Xalapa, Ver., México
| | - Miriam Pescador-Rojas
- Escuela Superior de Cómputo, Instituto Politécnico Nacional, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz s/n, esq. Av. Miguel Othón de Mendizabal, Col. Lindavista, Gustavo A. Madero, C. P. 07738, Ciudad de México, México
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Li Z, Zheng J, An B, Ma X, Ying F, Kong F, Wen J, Zhao G. Several models combined with ultrasound techniques to predict breast muscle weight in broilers. Poult Sci 2023; 102:102911. [PMID: 37494808 PMCID: PMC10393806 DOI: 10.1016/j.psj.2023.102911] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/20/2023] [Accepted: 06/27/2023] [Indexed: 07/28/2023] Open
Abstract
The weight of breast muscle (WBM) is a highly monitored indicator in broiler breeding that can be obtained after slaughtering. Currently, due to the lack of accurate in vivo phenotypes for both genomic and phenotypic selection, genetic gains in WBM fall short of initial expectations. In this study, 1,006 market-age (42 d) broilers from 3 generations over 2 yr were randomly selected, and the breast width (BW), fossil bone length (FBL), breast muscle thickness (BMT), and live weight (LW) were measured exactly in vivo. Eight models, including multiple linear regression (MLR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), and elastic net (EN), were fitted to explore the best regression relationships between breast muscle weight and these indicators. Support vector machine (SVM) methods with both linear kernels and radial kernels were used to fit the models, while 2 decision tree-based machine learning algorithms, random forest (RF), and extreme gradient boosting (XGBoost), were used to establish the prediction model. The predictive effects of different combinations of independent variables were compared, leading to the conclusion that the EN model achieves the best predictive power when all 4 live features are used as inputs and is slightly better than the other models (R2 = 0.7696). This method could be applied in practical production and breeding work, leading to substantial cost savings and enhancements in the breeding process.
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Affiliation(s)
- Zhengda Li
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jumei Zheng
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Bingxing An
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiaochun Ma
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Fan Ying
- Mile Xinguang Agricultural and Animal Industrials Corporation, Mile, China
| | - Fuli Kong
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jie Wen
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Guiping Zhao
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
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Rangel-Peña UJ, Zárate-Hernández LA, Camacho-Mendoza RL, Gómez-Castro CZ, González-Montiel S, Pescador-Rojas M, Meneses-Viveros A, Cruz-Borbolla J. Conceptual DFT, machine learning and molecular docking as tools for predicting LD 50 toxicity of organothiophosphates. J Mol Model 2023; 29:217. [PMID: 37380915 DOI: 10.1007/s00894-023-05630-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 06/21/2023] [Indexed: 06/30/2023]
Abstract
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 (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 R2 values for the training set (R2Train) and R2 values for the test set (R2Test), 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.
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Affiliation(s)
- Uriel J Rangel-Peña
- 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
| | - Luis A Zárate-Hernández
- 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
| | - Rosa L Camacho-Mendoza
- 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
| | - Carlos Z Gómez-Castro
- 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
| | - Simplicio González-Montiel
- 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
| | | | - Amilcar Meneses-Viveros
- Departamento de Computación, CINVESTAV-IPN, Av. IPN 2508, Col. San Pedro Zacatenco, Ciudad de Mexico, 07360, México
| | - Julián Cruz-Borbolla
- 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.
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Quantitative Measurements of Pharmacological and Toxicological Activity of Molecules. CHEMISTRY 2022. [DOI: 10.3390/chemistry4040097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Toxicity and pharmacological activity scales of molecules, in particular toxicants, xenobiotics, drugs, nutraceuticals, etc., are described by multiples indicators, and the most popular is the median lethal dose (LD50). At the molecular level, reversible inhibition or binding constants provide unique information on the potential activity of molecules. The important problem concerning the meaningfulness of IC50 for irreversible ligands/inhibitors is emphasized. Definitions and principles for determination of these quantitative parameters are briefly introduced in this article. Special attention is devoted to the relationships between these indicators. Finally, different approaches making it possible to link pharmacological and toxicological properties of molecules in terms of molecular interactions (or chemical reactions) with their biological targets are briefly examined. Experimental trends for future high-throughput screening of active molecules are pointed out.
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