A note on neighborhood first Zagreb energy and its significance as a molecular descriptor

The Peroxisome proliferators-activated receptors (PPARs) are one of the nuclear fatty acid receptors, which contain a type II zincfinger DNA binding pattern and a hydrophobic ligand binding pocket. These receptors are thought to play an essential role in metabolic diseasessuch as obesity, insulin resistance, and coronary artery disease. Therefore Peroxisome Proliferators-Activated Receptor (PPARγ) activators havedrawn great recent attention in the clinical management of type 2 diabetes mellitus, prompting several attempts to discover and optimize newPPARγ activators. Objective: The aim of the study was to finding new selective human PPARγ (PPARγ) modulators that are able to improveglucose homeostasis with reduced side effects compared with TZDs and identify the specific molecular descriptor and structural constraint toimprove the agonist activity of PPARγ analogs. Material and Method: Software’s that was used for this study include S.P. Gupta QSARsoftware (QSAR analysis), Valstat (Comparative QSAR analysis and calculation of L-O-O, Q2, r2, Spress), BILIN (Comparative QSAR analysisand calculation of Q2, r, S, Spress, and F), etc., allowing directly performing statistical analysis. Then multiple linear regression based QSARsoftware (received from BITS-Pilani, India) generates QSAR equations. Result and Discussion: In this study, we explored the quantitativestructure–activity relationship (QSAR) study of a series of meta-substituted Phenyl-propanoic acids as Peroxisome Proliferators Gamma activatedreceptor agonists (PPARγ).The activities of meta-substituted Phenyl-propanoic acids derivatives correlated with various physicochemical, electronic and steric parameters.Conclusion: The identified QSAR models highlighted the significance of molar refractivity and hydrophobicity to the biological activity.

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Faculty Opinions recommendation of How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718195292.793489228 ◽

2014 ◽

Author(s):

Suzanne Walker ◽

Lincoln Pasquina

Keyword(s):

Comparative Analysis ◽

Molecular Descriptor ◽

Assessment Methods ◽

Descriptor Space ◽

Diversity Assessment

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Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules

Pharmaceutics ◽

10.3390/pharmaceutics13071101 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1101

Author(s):

Elena M. Tosca ◽

Roberta Bartolucci ◽

Paolo Magni

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Molecular Descriptor ◽

Biological Properties ◽

Training Dataset ◽

Structure Property ◽

Feed Forward Neural Network ◽

Qspr Model ◽

Artificial Neural ◽

Intrinsic Solubility

Machine learning (ML) approaches are receiving increasing attention from pharmaceutical companies and regulatory agencies, given their ability to mine knowledge from available data. In drug discovery, for example, they are employed in quantitative structure–property relationship (QSPR) models to predict biological properties from the chemical structure of a drug molecule. In this paper, following the Second Solubility Challenge (SC-2), a QSPR model based on artificial neural networks (ANNs) was built to predict the intrinsic solubility (logS0) of the 100-compound low-variance tight set and the 32-compound high-variance loose set provided by SC-2 as test datasets. First, a training dataset of 270 drug-like molecules with logS0 value experimentally determined was gathered from the literature. Then, a standard three-layer feed-forward neural network was defined by using 10 ChemGPS physico-chemical descriptors as input features. The developed ANN showed adequate predictive performances on both of the SC-2 test datasets. Benefits and limitations of ML approaches have been highlighted and discussed, starting from this case-study. The main findings confirmed that ML approaches are an attractive and promising tool to predict logS0; however, many aspects, such as data quality, molecular descriptor computation and selection, and assessment of applicability domain, are crucial but often neglected, and should be carefully considered to improve predictions based on ML.

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Spatiotemporal Imaging and Pharmacokinetic of Fluorescent Compounds in Zebrafish Eleuthero-Embryos After Different Routes of Administration

10.21203/rs.3.rs-237483/v1 ◽

2021 ◽

Author(s):

Marlly Guarin ◽

Ruben Faelens ◽

Arianna Giusti ◽

Noémie De Croze ◽

Marc Léonard ◽

...

Keyword(s):

Molecular Descriptor ◽

False Negative ◽

Whole Body ◽

Pericardial Cavity ◽

Routes Of Administration ◽

Relationship Analysis ◽

Negative Results ◽

False Negative Results ◽

Parameter Values ◽

Fluorescent Compounds

Abstract Zebrafish (Danio rerio) is increasingly used to assess the pharmacological activity and toxicity of compounds. The spatiotemporal distribution of seven fluorescent alkyne compounds was examined during 48 h after immersion (10 µM) or microinjection (2 mg/kg) in the pericardial cavity (PC), intraperitoneally (IP) and yolk sac (IY) of 3 dpf zebrafish eleuthero-embryos. By modelling the fluorescence of whole-body contours present in fluorescence images, the main pharmacokinetic (PK) parameter values of the compounds were determined. It was demonstrated that especially in case of short incubations (1-3h) immersion can result in limited intrabody exposure to compounds. In this case, PC and IP microinjections represent excellent alternatives. Significantly, IY microinjections did not result in a suitable intrabody distribution of the compounds. Performing a QSPkR (quantitative structure-pharmacokinetic relationship) analysis, LogD was identified as the only molecular descriptor that explains the final uptake of the selected compounds. It was also shown that combined administration of compounds (immersion and microinjection) provides a more stable intrabody exposure, at least in case of a prolonged immersion and compounds with LogD value > 1. These results will help reduce the risk of false negative results and can offer an invaluable input for future translational research and safety assessment applications.

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Evaluation of Novel N-(Dibenzylcarbamothioyl)benzamide Derivatives as Antibacterial Agents by Using DFT and Drug-Likeness Assessment

Journal of Chemistry ◽

10.1155/2018/9176280 ◽

2018 ◽

Vol 2018 ◽

pp. 1-5 ◽

Cited By ~ 1

Author(s):

Huda Misral ◽

Suhaila Sapari ◽

Tajudin Rahman ◽

Nazlina Ibrahim ◽

Bohari M. Yamin ◽

...

Keyword(s):

Density Functional ◽

Molecular Descriptor ◽

Inhibition Zone ◽

Antibacterial Activities ◽

Computational Method ◽

Lipinski’S Rule ◽

H Nmr ◽

Elementary Analysis ◽

Benzoyl Isothiocyanate

Isomers of monothioureas, 2a–2d, derived from the reaction of disubstituted benzoyl isothiocyanate and dibenzylamine were synthesised and characterised by using elementary analysis CHNS and IR, 1H NMR, and 13C NMR spectroscopies. The compounds were screened for their in vitro antibacterial activity by using selected Gram-positive bacteria, and moderate inhibition activity was displayed for compound 2b with the value of inhibition zone 11 ± 0.8 mm at a concentration of 50 mg/ml. The outcomes of Lipinski’s rule of five assessment appeared to be in agreement with all compounds as they adhered to most of the rules, in which they can be preliminarily classified as active drug-like. The frontier molecular orbitals (HOMO and LUMO) for halogen-substituted 3,4-dichloro (2a) and 3,4-difluoro (2b) were also determined by applying the computational method of density functional theory (DFT) to determine their relationship as a molecular descriptor in antibacterial activities. The value of LUMO energy for compound 2b (1.8229 eV) is lower than that of compound 2a (1.8492 eV) which indicates higher antibacterial activities.

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Antiviral Essential Oil Components Against SARS-CoV-2 in Pre-procedural Mouth Rinses for Dental Settings During COVID-19: A Computational Study

Frontiers in Chemistry ◽

10.3389/fchem.2021.642026 ◽

2021 ◽

Vol 9 ◽

Author(s):

Pradeep Kumar Yadalam ◽

Kalaivani Varatharajan ◽

K. Rajapandian ◽

Priyanka Chopra ◽

Deepavalli Arumuganainar ◽

...

Keyword(s):

Essential Oil ◽

Oral Cavity ◽

Computational Study ◽

Molecular Descriptor ◽

Conceptual Dft ◽

Highly Active ◽

Dental Procedures ◽

Mouth Rinses ◽

Viral Particles ◽

Oil Components

COVID-19 mainly spreads through cough or sneeze droplets produced by an infected person. The viral particles are mostly present in the oral cavity. The risk of contracting COVID-19 is high in the dental profession due to the nature of procedures involved that produce aerosols. Along with other measures to limit the risk of infection, pre-procedural mouth rinses are beneficial in reducing the viral particles in the oral cavity. In this study, the antiviral efficacy of essential oil components has been determined specifically against SARS-CoV-2 by molecular docking and conceptual DFT approach. Based on the binding affinities of the components against the receptor binding domain of the S1 glycoprotein, cuminal, carvacrol, myrtanol, and pinocarveol were found to be highly active. The molecular descriptor values obtained through conceptual DFT also indicated the above-mentioned components to be active based on the correlation between the structure and the activity of the compounds. Therefore, pre-procedural mouth rinses with these components included may be specifically suitable for dental procedures during the COVID-19 period.

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A 2D-QSAR and Grid-Independent Molecular Descriptor (GRIND) Analysis of Quinoline-Type Inhibitors of Akt2: Exploration of the Binding Mode in the Pleckstrin Homology (PH) Domain

PLoS ONE ◽

10.1371/journal.pone.0168806 ◽

2016 ◽

Vol 11 (12) ◽

pp. e0168806 ◽

Cited By ~ 3

Author(s):

Noreen Akhtar ◽

Ishrat Jabeen

Keyword(s):

Molecular Descriptor ◽

Binding Mode ◽

Ph Domain ◽

Pleckstrin Homology ◽

2D Qsar

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The Olfactory System as a Molecular Descriptor

Physiology ◽

10.1152/physiologyonline.1987.2.6.203 ◽

1987 ◽

Vol 2 (6) ◽

pp. 203-208

Author(s):

J Chanel

Keyword(s):

Distribution Pattern ◽

Olfactory System ◽

Molecular Descriptor ◽

Olfactory Mucosa ◽

Membrane Receptor ◽

Receptor Sites

The olfactory mucosa has a limited variety of membrane receptor sites, diversely distributed from one olfactory cell to another. The distribution pattern might provide an activation scheme that is spatially organized for each odorant. The distinct topological arrangement would then constitute a molecular descriptor of the odorant.

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