Prediction of spin–spin coupling constants with machine learning in NMR
Analytical Science Advances, 2021 Nuclear magnetic resonance (NMR) spectroscopy is one of the most important methods for analyzing the molecular structures of compounds. The objective in this study is to predict indirect spin–spin coupling constants in NMR based on machine learning.
Kaina Shibata, Hiromasa Kaneko
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On Computation of Edge Degree-Based Banhatti Indices of a Certain Molecular Network
Journal of Mathematics, 2021 Chemical graph theory deals with the basic properties of a molecular graph. In graph theory, we correlate molecular descriptors to the properties of molecular structures.
Jiang-Hua Tang +6 more
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Heliyon, 2021 To be able to predict reversed phase liquid chromatographic (RPLC) retention times of contaminants is an asset in order to solve food contamination issues.
Julien Parinet
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Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials. [PDF]
Journal of Chemical Theory and Computation, 2020 Machine learning techniques allow a direct mapping of atomic positions and nuclear charges to the potential energy surface with almost ab-initio accuracy and the computational efficiency of empirical potentials. In this work we propose a machine learning
Viktor Zaverkin, Johannes Kästner
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Comparison of Molecular Geometry Optimization Methods Based on Molecular Descriptors
Mathematics, 2021 Various methods (Hartree–Fock methods, semi-empirical methods, Density Functional Theory, Molecular Mechanics) used to optimize a molecule structure feature the same basic approach but differ in the mathematical approximations used.
Donatella Bálint, Lorentz Jäntschi
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Systems biological approach of molecular descriptors connectivity: optimal descriptors for oral bioavailability prediction. [PDF]
PLoS ONE, 2012 Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability.
Shiek S S J Ahmed, V Ramakrishnan
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IN SILICO EVALUATION OF ANGIOTENSIN II RECEPTOR ANTAGONIST’S PLASMA PROTEIN BINDING USING COMPUTED MOLECULAR DESCRIPTORS [PDF]
, 2014 The discovery of new pharmacologically active substances and drugs modeling led to necessity of predicting drugs properties and its ADME data. Angiotensin II receptor antagonists are a group of pharmaceuticals which modulate the renin-angiotensin ...
Jadranka Odović +1 more
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Predicting Skin Permeability by means of Computational Approaches : Reliability and Caveats in Pharmaceutical Studies [PDF]
, 2019 © 2019 American Chemical Society.The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters.
Almerico, Anna Maria +5 more
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Computer Aided Aroma Design. II. Quantitative structure-odour relationship [PDF]
, 2008 Computer Aided Aroma Design (CAAD) is likely to become a hot issue as the REACH EC document targets many aroma compounds to require substitution. The two crucial steps in CAMD are the generation of candidate molecules and the estimation of properties ...
Floquet, Pascal +5 more
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Relationship between the bioavailability and molecular properties of angiotensin II receptor antagonists [PDF]
Archives of Biological Sciences, 2016 In the present study, we investigated the relationships between several molecular properties and bioavailability data for seven of the most commonly prescribed angiotensin II receptor antagonists (also known as angiotensin II receptor blockers ...
Trbojević Jovana B. +4 more
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