Structure-odor relationship in pyrazines and derivatives: A physicochemical study using 3D-QSPR, HQSPR, Monte Carlo, molecular docking, ADME-Tox and molecular dynamics

OUABANE, Mohamed; TABTI, Kamal; HAJJI, Halima; ELBOUHI, Mhamed; KHALDAN, Ayoub; ELKAMEL, Khalid; SBAI, Abdelouahid; Aziz AJANA, Mohammed; SEKKATE, Chakib; BOUACHRINE, Mohammed; LAKHLIFI, Tahar

doi:10.1016/j.arabjc.2023.105207

Cited by 11 publications

(3 citation statements)

References 22 publications

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“…The main advantage of the Monte Carlo method is the use of randomness to solve problems that, in principle, may be more or less deterministic. Nowadays, computers easily supply randomness to the interested user through random variable generators [50][51][52][53][54][55][56][57][58][59][60]. The models considered are certainly largely random.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Structure–Activity Relationship Models for the Angiotensin-Converting Enzyme Inhibitory Activities of Short-Chain Peptides of Goat Milk Using Quasi-SMILES

Toropova,

Toropov,

Roncaglioni

et al. 2024

Macromol

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The inhibitory activity of peptides on angiotensin-converting enzyme (ACE) is a measure of their antihypertensive potential. Quantitative structure–activity relationship (QSAR) models obtained based on the analysis of sequences of amino acids are suggested. The average determination coefficient for the active training sets is 0.36 ± 0.07. The average determination coefficient for validation sets is 0.79 ± 0.02. The paradoxical situation is caused by applying the vector of ideality of correlation, which improves the statistical quality of a model for the calibration and validation sets but is detrimental to the statistical quality of models for the training sets.

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Section: Discussionmentioning

confidence: 99%

Quantitative Structure–Activity Relationship Models for the Angiotensin-Converting Enzyme Inhibitory Activities of Short-Chain Peptides of Goat Milk Using Quasi-SMILES

Toropova,

Toropov,

Roncaglioni

et al. 2024

Macromol

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show abstract

“…The HQSPR method is similar to other QSPR modeling methods in terms of presenting molecules by their correspondent molecular descriptors. However, it differs by using molecular holograms, which are more expressive and capture more complex information than traditional molecular descriptors [15] . In addition, HQSPR uses a multiple linear regression method to establish a quantitative relationship between molecular holograms and biological properties of molecules [16] …”

Section: Methodsmentioning

confidence: 99%

Exploring Structure–Toxicity Relationships in Nitrobenzene and Derivatives: A Multifaceted Biochemical Investigation Using 3D–QSPR, HQSPR, Molecular Docking, and MD Simulation

Ouabane,

Zaki,

Alaqarbeh

et al. 2024

ChemistrySelect

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Nitrobenzene derivatives present an environmental concern to aquatic organisms, as well as a potential adverse effects on human health due to their toxicity. Therefore, monitoring nitrobenzene contamination levels is critical to protect both the ecosystem and the public. This study aims to quantitatively correlate the toxicity of nitrobenzene derivatives to their chemical structures using computational chemistry methods such as 3D‐QSPR, HQSPR, Molecular Docking and MD simulation. Additionally, to investigate the type of interactions and stability of nitrobenzene derivatives with the PP2 protein involved in the Phloem protein 2 (PP2). From each computational method a prediction model was extracted HQSPR (Atomic, Bonds, Donor/Acceptor, 3D‐QSPR (Steric, Electrostatic, Hydrophobic, Acceptor), and used for the design of X1 and X2 new nitrobenzene derivatives, which showed remarkable stability and better interactions. This study contributes to the understanding of the nitrobenzene derivatives’ toxicity and offers materials towards the understanding of the mechanism of action of these substances in protein–ligand interactions.

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“…Periodic Boundary Conditions (PBC) are set in the third step and approximate a large system using a unit cell replicated in all directions. [47] The simulation takes place only for the atoms inside the PBC box. Bad contacts are removed in this step by running short minimization.…”

Section: Molecular Dynamics Simulationsmentioning

confidence: 99%

Quality Control of Coumarins, Furocoumarins and Polymethoxyflavones in Citrus Essential Oils: In Silico Analysis

Ouabane.,

Alaqarbeh,

Hajji

et al. 2024

ChemistrySelect

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Citrus essential oils (EOs) contain a variety of secondary metabolites, including coumarins (Cs), furocoumarins (FCs) and polymethoxyflavones (PMFs), all with different biological activities. While Regulation (EC) No 1334/2008 strictly regulates the use of coumarins, undesirable side effects of consuming furocoumarins remain without established maximum toxicity limits for human dietary intake. This study is based on an in‐silico approach aimed at determining these toxicity limits for the oxygenated heterocyclic compounds of citrus EOs approved for use in food. In addition, an innovative quantitative structure‐property relationship (QSPR) using the linear retention index system was explored to characterize the oxygenated heterocyclic compounds (COHs) present in these essential oils. Molecular docking of the studied compounds provided crucial insights into the structure‐odor relationships, offering insights into their mechanisms of action. In addition, an analysis of the pharmacokinetic and pharmacodynamic properties through ADME‐Tox deconstruction provides insight into the potential side effects of their consumption. The results are consolidated by dynamic simulations, providing crucial data for the development of regulations on the maximum toxicity limits of citrus EOs, given the health implications of the various compounds such as coumarins, furocoumarins and polymethoxyflavones present in these essential oils.

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Structure-odor relationship in pyrazines and derivatives: A physicochemical study using 3D-QSPR, HQSPR, Monte Carlo, molecular docking, ADME-Tox and molecular dynamics

Cited by 11 publications

References 22 publications

Quantitative Structure–Activity Relationship Models for the Angiotensin-Converting Enzyme Inhibitory Activities of Short-Chain Peptides of Goat Milk Using Quasi-SMILES

Quantitative Structure–Activity Relationship Models for the Angiotensin-Converting Enzyme Inhibitory Activities of Short-Chain Peptides of Goat Milk Using Quasi-SMILES

Exploring Structure–Toxicity Relationships in Nitrobenzene and Derivatives: A Multifaceted Biochemical Investigation Using 3D–QSPR, HQSPR, Molecular Docking, and MD Simulation

Quality Control of Coumarins, Furocoumarins and Polymethoxyflavones in Citrus Essential Oils: In Silico Analysis

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