Quantitative Structure-Property Relationship (QSPR) models for modeling and predicting thermodynamic properties such as the enthalpy of vaporization at standard condition (ΔH˚vap kJ mol-1) and normal temperature of boiling points (T˚bp K) of 57 mono and Polycyclic Aromatic Hydrocarbons (PAHs) have been investigated. The PAHs were randomly separated into 2 groups: training and test sets. A set o...

High performance liquid chromatography (HPLC) with ultraviolet (UV) spectrophotometric detection is a common method for analyzing reaction products in organic chemistry. This procedure would benefit from a computational model for predicting the relative response of organic molecules. Models are now reported for the prediction of the integrated UV absorbance for a diverse set of organic compound...

The support vector machine (SVM), as a novel type of learning machine, for the first time, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the heat capacity of a diverse set of 182 compounds based on the molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function networks (RBFNNs) were also utilized to ...

Ketones are one of the most common functional groups, and ketone-containing compounds are essential in both the nature and the chemical sciences. As such, the acidities (pKa) of ketones provide valuable information for scientists to screen for biological activities, to determine physical properties or to study reaction mechanisms. Direct measurements of pKa of ketones are not readily available ...

The potential utility of data reduction methods (e.g. principal component analysis) for the analysis of matrices assembled from the related properties of large sets of compounds is discussed by reference to results obtained from solvent polarity scales, ongoing work on solubilities and sweetness properties, and proposed general treatments of toxicities and gas chromatographic retention indices.

Ionic liquids (ILs) have attracted large amount of interest due to their unique properties. Although large effort has been focused on the investigation f their potential application, characterization of ILs properties and structure–property relationships of ILs are poorly understood. Computer aided olecular design (CAMD) of ionic liquids (ILs) can only be carried if predictive computational met...

Applied datasets can vary from a few hundred to thousands of samples in typical quantitative structure-activity/property (QSAR/QSPR) relationships and classification. However, the size train/test split ratios greatly affect outcome models, thus classification performance itself. We compared several combinations dataset sizes with five different machine learning algorithms find differences or si...

A quantitative structure-property relationship (QSPR) was used to design model protein sequences that fold repeatedly and relatively rapidly to stable target structures. The specific model was a 125-residue heteropolymer chain subject to Monte Carlo dynamics on a simple cubic lattice. The QSPR was derived from an analysis of a database of 200 sequences by a statistical method that uses a geneti...

Aqueous solubility of a drug/drug candidate is essential data in drug discovery, and an in silico method for predicting the aqueous solubility of drug candidates provides a valuable tool to speed up the process of drug discovery and development. This paper describes a simple quantitative structure property relationship (QSPR) model for predicting the aqueous solubility of drugs which is validat...