Molecular toxicity of nitrobenzene derivatives to tetrahymena pyriformis based on SMILES descriptors using Monte Carlo, docking, and MD simulations.

Autor: Ouabane M; Molecular Chemistry and Natural Substances Laboratory, Department of Chemistry, Faculty of Science, Moulay Ismail University, Meknes, Morocco; Chemistry-Biology Applied to the Environment URL CNRT 13, Chemistry Department, Faculty of Science, Moulay Ismail University, Meknes, Morocco., Zaki K; Molecular Chemistry and Natural Substances Laboratory, Department of Chemistry, Faculty of Science, Moulay Ismail University, Meknes, Morocco., Tabti K; Molecular Chemistry and Natural Substances Laboratory, Department of Chemistry, Faculty of Science, Moulay Ismail University, Meknes, Morocco., Alaqarbeh M; Basic Science Department, Prince Al Hussein Bin Abdullah II Academy for Civil Protection, Al-Balqa Applied University, Al-Salt, 19117, Jordan., Sbai A; Molecular Chemistry and Natural Substances Laboratory, Department of Chemistry, Faculty of Science, Moulay Ismail University, Meknes, Morocco., Sekkate C; Chemistry-Biology Applied to the Environment URL CNRT 13, Chemistry Department, Faculty of Science, Moulay Ismail University, Meknes, Morocco., Bouachrine M; Molecular Chemistry and Natural Substances Laboratory, Department of Chemistry, Faculty of Science, Moulay Ismail University, Meknes, Morocco; Higher School of Technology-Khenifra (EST-Khenifra), University of Sultan Moulay Slimane, PB 170, Khenifra, 54000, Morocco., Lakhlifi T; Molecular Chemistry and Natural Substances Laboratory, Department of Chemistry, Faculty of Science, Moulay Ismail University, Meknes, Morocco. Electronic address: t.lakhlifi@umi.ac.ma.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Feb; Vol. 169, pp. 107880. Date of Electronic Publication: 2023 Dec 25.
DOI: 10.1016/j.compbiomed.2023.107880
Abstrakt: It is challenging to model the toxicity of nitroaromatic compounds due to limited experimental data. Nitrobenzene derivatives are commonly used in industry and can lead to environmental contamination. Extensive research, including several QSPR studies, has been conducted to understand their toxicity. Predictive QSPR models can help improve chemical safety, but their limitations must be considered, and the molecular factors affecting toxicity should be carefully investigated. The latest QSPR methods, molecular modeling techniques, machine learning algorithms, and computational chemistry tools are essential for developing accurate and robust models. In this work, we used these methods to study a series of fifty compounds derived from nitrobenzene. The Monte Carlo approach was used for QSPR modeling by applying the SMILES molecular structure representation and optimal molecular descriptors. The correlation ideality index (CII) and correlation contradiction index (CCI) were further introduced as validation parameters to estimate the developed models' predictive ability. The statistical quality of the CII models was better than those without CII. The best QSPR model with the following statistical parameters (Split-3): (R 2  = 0.968, CCC = 0.984, IIC = 0.861, CII = 0.979, Q 2  = 0.954, Q F1 2  = 0.946, Q F2 2  = 0.938, Q F3 2  = 0.947, R m 2  = 0.878, RMSE = 0.187, MAE = 0.151, F Training  = 390, F Invisible  = 218, F Calibration  = 240, R Test 2  = 0.905) was selected to generate the studied promoters with increasing and decreasing activity.
Competing Interests: Declaration of competing interest We, the authors of the manuscript titled “Molecular toxicity of nitrobenzene derivatives to Tetrahymena pyriformis based on SMILES descriptors using Monte Carlo, Docking and MD simulations”, submitted to your journal, declare the following potential conflicts of interest that could be perceived as influencing the results and interpretations presented in our research. We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work presented in this article. However, we would like to mention the following financial interests and personal relationships, which may be considered potential competing interests but do not create any direct conflicts of interest with the content of the submitted manuscript: We want to emphasize that we have no conflicts of interest that could bias the results or interpretation of this research study. We affirm that the manuscript has been prepared in an unbiased and rigorous manner, and the reported results and conclusions are based solely on the presented data and analyses. No financial or personal interests have influenced the results of this study, preserving its integrity and objectivity. Furthermore, we confirm that this manuscript has not been previously published in any other publication and is not currently under review by any other journal. We fully understand that complete transparency regarding potential conflicts of interest is essential to maintain the credibility and integrity of scientific research. If this manuscript is accepted for publication in Computers in Biology and Medicine, we commit to promptly inform the editorial committee of any changes in our declaration of interests that may arise during the revision process or before publication.
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Databáze: MEDLINE
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