Fundamental physical and chemical concepts behind “drug-likeness” and “natural product-likeness”

Athar, Mohd; Sona, Alfred N.; Bekono, Boris D.; Ntie‐Kang, Fidele

doi:10.1515/psr-2018-0101

Cited by 13 publications

(16 citation statements)

References 70 publications

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“…However, in comparison to abundant studies of drug-likeness, the research of pesticidelikeness is still lagging behind. (1) The current studies on pesticide-likeness parameters are far from enough, such as dipole moment (vector), electron polarizability (tensor), electrostatic potential energy gradient (vector field), and electrostatic potential (scalar field). Besides, many parameters, such as molecular topology descriptors, shape descriptors, and multi-dimensional structure parameters, are also worth studying.…”

Section: What Are We Lacking In Pesticide-likeness?mentioning

confidence: 98%

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In Silico Resources of Drug-Likeness as a Mirror: What Are We Lacking in Pesticide-Likeness?

Ouyang

Huang

Wang

et al. 2021

J. Agric. Food Chem.

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Unfavorable bioavailability is an important aspect underlying the failure of drug candidates. Computational approaches for evaluating drug-likeness can minimize these risks. Over the past decades, computational approaches for evaluating drug-likeness have sped up the process of drug development and were also quickly derived to pesticide-likeness. As a result of many critical differences between drugs and pesticides, many kinds of methods for drug-likeness cannot be used for pesticide-likeness. Therefore, it is crucial to comprehensively compare and analyze the differences between drug-likeness and pesticide-likeness, which may provide a basis for solving the problems encountered during the evaluation of pesticide-likeness. Here, we systematically collected the recent advances of drug-likeness and pesticide-likeness and compared their characteristics. We also evaluated the current lack of studies on pesticide-likeness, the molecular descriptors and parameters adopted, the pesticide-likeness model on pesticide target organisms, and comprehensive analysis tools. This work may guide researchers to use appropriate methods for developing pesticide-likeness models. It may also aid non-specialists to understand some important concepts in drug-likeness and pesticide-likeness.

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Section: What Are We Lacking In Pesticide-likeness?mentioning

confidence: 98%

“…To address this issue, the computational approaches of drug-likeness were introduced into the drug development process. 1 In candidate drug pharmacokinetics, the attrition rate has become reduced from 39 to 7%. 2 In contrast, the process of optimizing pesticide candidates into a pesticide is more challenging.…”

Section: Introductionmentioning

confidence: 99%

In Silico Resources of Drug-Likeness as a Mirror: What Are We Lacking in Pesticide-Likeness?

Ouyang

Huang

Wang

et al. 2021

J. Agric. Food Chem.

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“…Drug-likeness and Bioactivity: A compound's drug-likeness is determined by how similar it is to existing drugs in terms of structural and physicochemical features, and it serves as the foundation for future drug development [45]. In terms of drug-likeness, the reference and lead compounds do not violate the Ghose, Lipinski, and Veber rules seeing that the values of their HBA, HBD, log P, molecular weight, TPSA, molar refractivity, and number of rotatable bonds are within accepted range [46].…”

Section: Discussionmentioning

confidence: 99%

Identifying potential natural inhibitors of Brucella melitensis Methionyl-tRNA synthetase through an in-silico approach

Rowaiye

Ogugua

Ibeanu³

et al. 2021

Preprint

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Background: Brucellosis is an infectious disease caused by bacteria of the genus Brucella. Although it is the most common zoonosis worldwide, there are increasing reports of drug resistance and cases of relapse after long term treatment with the existing drugs of choice. This study therefore aims at identifying possible natural inhibitors of Brucella melitensis Methionyl-tRNA synthetase through an in-silico approach. Methods: Using PyRx 0.8 virtual screening software, the target was docked against a library of natural compounds obtained from edible African plants. The compound, 2-({3-[(3,5-dichlorobenzyl) amino] propyl} amino) quinolin-4(1H)-one (OOU) which is a co-crystallized ligand with the target was used as the reference compound. Screening of the molecular descriptors of the compounds for bioavailability, pharmacokinetic properties, and bioactivity was performed using the SWISSADME, pkCSM, and Molinspiration web servers respectively. The Fpocket and PLIP webservers were used to perform the analyses of the binding pockets and the protein ligand interactions. Analysis of the time-resolved trajectories of the Apo and Holo forms of the target was performed using the Galaxy and MDWeb servers. The lead compounds, Strophanthidin and Isopteropodin are present in Corchorus olitorius and Uncaria tomentosa (cat-claw) plants respectively. Results: Isopteropodin had a binding affinity score of -8.9 kcal / ml with the target and had 17 anti-correlating residues in pocket 1 after molecular dynamics simulation. The complex formed by Isopteropodin and the target had a total RMSD of 4.408 and a total RMSF of 9.8067. However, Strophanthidin formed 3 hydrogen bonds with the target at ILE21, GLY262 and LEU294, and induced a total RMSF of 5.4541 at Pocket 1. Conclusion: Overall, Isopteropodin and Strophanthidin were found to be better drug candidates than OOU and they showed potentials to inhibit the Brucella melitensis Methionyl-tRNA synthetase at Pocket 1, hence abilities to treat brucellosis. In vivo and in vitro investigations are needed to further evaluate the efficacy and toxicity of the lead compounds.

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“…Pharmacological drug-likeness is a groundbreaking evaluation of the potential of a particular chemical used as an oral medicine with respect to bioavailability. It is estimated that nine out of twelve targeted medicines are not transparently changed due to their negative effect, resulting in significant medication costs, time, and human resources being wasted [62]. This problem occurs due to failure to identify the actual drug characteristics.…”

Section: Pharmacokinetics and Drug-likeness Studymentioning

confidence: 99%

Investigation of the New Inhibitors by Sulfadiazine and Modified Derivatives of α-D-glucopyranoside for White Spot Syndrome Virus Disease of Shrimp by In Silico: Quantum Calculations, Molecular Docking, ADMET and Molecular Dynamics Study

et al. 2022

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The α-D-glucopyranoside and its derivatives were as the cardinal investigation for developing an effective medication to treat the highest deadly white spot syndrome virus (WSSV) diseases in Shrimp. In our forthcoming work, both computational tools, such as molecular docking, quantum calculations, pharmaceutical kinetics, ADMET, and their molecular dynamics, as well as the experimental trial against WSSV, were executed to develop novel inhibitors. In the beginning, molecular docking was carried out to determine inhibitors of the four targeted proteins of WSSV (PDB ID: 2ED6, 2GJ2, 2GJI, and 2EDM), and to determine the binding energies and interactions of ligands and proteins after docking. The range of binding affinity was found to be between −5.40 and −7.00 kcal/mol for the protein 2DEM, from −5.10 to 6.90 kcal/mol for the protein 2GJ2, from −4.70 to −6.2 kcal/mol against 2GJI, and from −5.5 kcal/mol to −6.6 kcal/mol for the evolved protein 2ED6 whereas the L01 and L03 display the highest binding energy in the protein 2EDM. After that, the top-ranked compounds (L01, L02, L03, L04, and L05), based on their high binding energies, were tested for molecular dynamics (MD) simulations of 100 ns to verify the docking validation and stability of the docked complex by calculating the root mean square deviation (RMSD) and root mean square fluctuation (RMSF). The molecules with the highest binding energy were then picked and compared to the standard drugs that were been applied to fish experimentally to evaluate the treatment at various doses. Consequently, approximately 40–45% cure rate was obtained by applying the dose of oxytetracycline (OTC) 50% with vitamin C with the 10.0 g/kg feed for 10 days. These drugs (L09 to L12) have also been executed for molecular docking to compare with α-D-glucopyranoside and its derivatives (L01 to L08). Next, the evaluation of pharmacokinetic parameters, such as drug-likeness and Lipinski’s principles; absorption; distribution; metabolism; excretion; and toxicity (ADMET) factors, were employed gradually to further evaluate their suitability as inhibitors. It was discovered that all ligands (L01 to L12) were devoid of hepatotoxicity, and the AMES toxicity excluded L05. Additionally, all of the compounds convey a significant aqueous solubility and cannot permeate the blood-brain barrier. Moreover, quantum calculations based on density functional theory (DFT) provide the most solid evidence and testimony regarding their chemical stability, chemical reactivity, biological relevance, reactive nature and specific part of reactivity. The computational and virtual screenings for in silico study reveals that these chosen compounds (L01 to L08) have conducted the inhibitory effect to convey as a possible medication against the WSSV than existing drugs (L09, L10, L11 and L12) in the market. Next the drugs (L09, L10, L11 and L12) have been used in trials.

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Fundamental physical and chemical concepts behind “drug-likeness” and “natural product-likeness”

Cited by 13 publications

References 70 publications

In Silico Resources of Drug-Likeness as a Mirror: What Are We Lacking in Pesticide-Likeness?

In Silico Resources of Drug-Likeness as a Mirror: What Are We Lacking in Pesticide-Likeness?

Identifying potential natural inhibitors of Brucella melitensis Methionyl-tRNA synthetase through an in-silico approach

Investigation of the New Inhibitors by Sulfadiazine and Modified Derivatives of α-D-glucopyranoside for White Spot Syndrome Virus Disease of Shrimp by In Silico: Quantum Calculations, Molecular Docking, ADMET and Molecular Dynamics Study

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