Our aim is to develop an effective computational procedure for predicting the aqueous acid equilibrium constants of protonated benzimidazoles at 298.15 K. The experimental determination of these values, apart from been laborious, is a challenge because of the low water solubility of these compounds. Using a variety of descriptors, quantitative structure-property relationships (QSPR) are explore...

A machine learning-quantitative structure property relationship (ML-QSPR) method is proposed to predict 15 fuel physicochemical properties of 23 types. QSPR-UOB 3.0 functional group classification system developed extract and digitalize the molecular feature. ML algorithms are used map feature as well model parameter tuning. UOB Fuel Property Database (1797 pure compounds 465 mixtures) establis...

Quantitative structure-property relationship (QSPR) modeling is crucial in cheminformatics and computational drug discovery for predicting the activity of compounds. Topological indices are a popular molecular descriptor QSPR due to their ability concisely capture structural electronic properties molecules. Here, we investigate use curvilinear regression models analyze fibrates through topologi...

We predict the soil sorption coefficient for a heterogeneous set of 643 organic non-ionic compounds by means of Quantitative Structure-Property Relationships (QSPR). A conformation-independent representation of the chemical structure is established. The 17,538 molecular descriptors derived with PaDEL and EPI Suite softwares are simultaneously analyzed through linear regressions obtained with th...

A data set comprising of the selectivity index of pentachlorophenol-imprinted polymers against 53 pentachlorophenol and related compounds was obtained from the excellent work of Baggiani et al. Molecular descriptors of the phenol compounds were calculated with EDRAGON to obtain a total of 1,666 descriptors spanning 20 categories of molecular properties. Multivariate analysis of the data set was...

A quantiatative structure property relationship (QSPR) treatment was used to a data set consisting of diverse 3-hydroxypyridine-4-one derivatives to relate the logarithmic function of octanol:water partition coefficients (denoted by log po/w) with theoretical molecular descriptors. Evaluation of a test set of 6 compounds with the developed partial least squares (PLS) model revealed that this mo...

There is considerable interest in prediction of reactive hazards based on chemical structure. Calorimetric measurements to determine reactivity can be resource consuming, so computational methods to predict reactivity hazards present an attractive option. This paper reviews some of the commonly employed theoretical hazard evaluation techniques, including the oxygen-balance method, ASTM CHETAH, ...

Group electronegativities, which take into account the influence of chemical environment, are calculated. Equilibrium electronegativity is defined on the basis of group electronegativity. Using relative bond lengths and equilibrium electronegativies, we create a novel semiempirical topological descriptor, Nt. A quantitative structure-property relationship (QSPR) model is subsequently developed ...

Self-assembled nano-micelles of amphiphilic polymers represent a novel anticancer drug delivery system. However, their full clinical utilization remains challenging because the quantitative structure-property relationship (QSPR) between the polymer structure and the efficacy of micelles as a drug carrier is poorly understood. Here, we developed a series of QSPR models to account for the drug lo...

Quantitative structure-activity relationship (QSAR) modeling pertains to the construction of predictive models of biological activities as a function of structural and molecular information of a compound library. The concept of QSAR has typically been used for drug discovery and development and has gained wide applicability for correlating molecular information with not only biological activiti...