a robust linear quantitative structure-property relationship (qspr) model has been constructed to model and predict the refractivity indices of 101 organic compounds as common halo-derivatives of normal paraffin by application of the structural descriptors combined with multiple linear regression (mlr) method. in the main part of this study, theoretical molecular descriptors were adopted from t...

Machine learning tools have been developed to analyze quantitative structure-activity/property relationship (QSAR/QSPR) modeling research. Better feature selection algorithms in the ensemble methods used advance QSPR/QSAR modeling, helping understand relation between features and target variables reducing computational requirements. Implementing importance allows for a more effective clearer vi...

the dispersibility of graphene is modeled as a mathematical function of the molecular structure of solvent represented by simplified molecular input-line entry systems (smiles) together with the graph of atomic orbitals (gao). the gao is molecular graph where atomic orbitals e.g. 1s1, 2p4, 3d7 etc., are vertexes of the graph instead of the chemical elements used as the graph vertexes in the tra...

A quantitative structure property relationship study of the flash point of a diverse set of 271 compounds provided a general three-parameter QSPR model (R(2) = 0.9020, R(2)(cv) = 0.8985, s = 16.1). Use of the experimental boiling point as a descriptor gives a three-descriptor equation with R(2) = 0.9529. Use of the boiling point predicted by a four-parameter reported relationship gives a three-...

Quantitative structure-activity relationship (QSAR) correlations have been widely applied for biological activities over several decades.1-7 Also, many applications of quantitative structure-property relationships (QSPR) are known in analytical chemistry.3,8-14 For instance, we recently successfully used15 our CODESSA (ComprehensiVe Descriptors for Structural and Statistical Analysis) QSPR prog...

The aim of this paper is to introduce the reader to new developments in Neural Networks and Kernel Machines concerning the treatment of structured domains. Specifically, we discuss the research on these relatively new models to introduce a novel and more general approach to QSPR/QSAR analysis. The focus is on the computational side and not on the experimental one.

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We consider two different methods for QSAR/QSPR regression tasks: Recursive Neural Networks (RecNN) and a Support Vector Regression (SVR) machine using a Tree Kernel. Experimental results on two specific regression tasks involving alkanes and benzodiazepines are obtained for the two approaches.

Yoctosecond Quantitative Structure–Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) under synchrotron radiations using Genetic Function Approximation (GFA) algorithm studies are suggested for the prediction of solubility of anti–cancer Nano drugs in aqueous solutions in yoctosecond [1-16]. Ab initio and density functional theories were used to calculate some ...

OBJECTIVES The aim was to employ Gaussian processes to assess mathematically the nature of a skin permeability dataset and to employ these methods, particularly feature selection, to determine the key physicochemical descriptors which exert the most significant influence on percutaneous absorption, and to compare such models with established existing models. METHODS Gaussian processes, includ...