How can polydispersity information be integrated in the QSPR modeling of mechanical properties?
Science and Technology of Advanced Materials: Methods, 2022 Polymer informatics is an emerging discipline that has benefited from the strong development that data science has experienced over the last decade. Machine learning methods are useful to infer QSPR (Quantitative Structure-Property Relationships) models ...
F. Cravero +4 more
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PREDICTION OF LIPOPHILIC PROPERTIES OF ADAMANTANE DERIVATIVES
Siberian Journal of Life Sciences and Agriculture, 2020 The article explores QSPR models for predicting the lypophilicity of chemicals in the adamantane family. The study of the lypophilicity parameter is carried out using the developed nonlinear models using absolute entropy.
Alexander Leonidovich Osipov +1 more
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New structure-based models for the prediction of normal boiling point temperature of ternary azeotropes [PDF]
Journal of the Serbian Chemical Society, 2021 Recently, development of the QSPR models for mixtures has received much attention. The QSPR modelling of mixtures requires the use of the appropriate mixture descriptors.
Faramarzi Zohreh +3 more
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QSPR and Nano-QSPR: Which One Is Common? The Case of Fullerenes Solubility
Inorganics, 2023 Background: The system of self-consistent models is an attempt to develop a tool to assess the predictive potential of various approaches by considering a group of random distributions of available data into training and validation sets. Considering many different splits is more informative than considering a single model.
Alla P. Toropova +2 more
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Quantitative Structure-Properties Relationship of Lubricating Oil Additives and Molecular Dynamic Simulations Studies of Diamond-Like-Carbon (DLC) [PDF]
Iranian Journal of Chemistry & Chemical Engineering, 2020 Quantitative Structure-Properties Relationship (QSPR) and molecular dynamics simulations studies were carried out on the 53 lubricating oil additives and hydrogen-containing DLC (a-C: H).
Usman Abdulfatai +3 more
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QSPR designer – a program to design and evaluate QSPR models. Case study on pKaprediction [PDF]
Journal of Cheminformatics, 2011 Nowadays, a large amount of experimental and predicted data about the 3D structure of organic molecules and biomolecules is available. Advanced computational methods and high performance computers allow us to obtain large sets of descriptors that can be used to estimate physicochemical properties.
Ondrej Skrehota +6 more
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Corrections of Molecular Morphology and Hydrogen Bond for Improved Crystal Density Prediction
Molecules, 2019 Density prediction is of great significance for molecular design of energetic materials, since detonation velocity linearly with density and detonation pressure increases with the density squared.
Linyuan Wang +7 more
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Estimating flavonoid oxidation potentials: mechanisms and charge-related regression models
Arhiv za Higijenu Rada i Toksikologiju, 2023 In this paper, I tested our quadratic regression models for the estimation of flavonoid oxidation potentials based on spin populations, the differences in the net atomic charges between a cation and a neutral flavonoid, between a radical and an anion of ...
Miličević Ante
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A simple 2D-QSPR model for the prediction of Setschenow constants of organic compounds
Macedonian Journal of Chemistry and Chemical Engineering, 2016 A quantitative structure-property relationship (QSPR) analysis of the Setschenow constants (Ksalt) of organic compounds in a sodium chloride solution was carried out using only two-dimensional (2D) descriptors as input parameters.
Qi Xu, Lingling Fan, Jie Xu
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Zhejiang Daxue xuebao. Lixue ban, 2005 利用多元线性回归和偏最小二乘分析方法，研究分子表面静电势参数，VolSurf参数与双十四酰基磷脂酰基胆碱(DMPC)-水分配系数之间的定量结构-性质关系(QSPR)，均得到较好的结果.分子表面静电势参数分析表明化合物分子表面上负的静电势越分散，越倾向于分配在水相中；与水分子的作用越强，越有利于分配在水相中.此外，具有较大体积和偶极密度的分子倾向于分配在弱极性相中.VolSurf参数分析表明大体积的分子易于分配在DMPC中;分子表面亲水区域较大的分子则倾向于分配在水相中 ...
HUGui-xiang(胡桂香) +5 more
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