A General Treatment of Solubility 4. Description and Analysis of a PCA Model for Ostwald Solubility Coefficients

Article discussing a general treatment of solubility and a description and analysis of a principal component analysis (PCA) model for Ostwald solubility coefficients

Quantitative Structure–Property Relationship Studies on Ostwald Solubility and Partition Coefficients of Organic Solutes in Ionic Liquids

Article discussing quantitative structure-property relationship studies on Ostwald solubility and partition coefficients of organic solutes in ionic liquids

Identification of Natural Compounds against Neurodegenerative Diseases Using In Silico Techniques

The aim of this study was to identify new potentially active compounds for three protein targets, tropomyosin receptor kinase A (TrkA), N-methyl-d-aspartate (NMDA) receptor, and leucine-rich repeat kinase 2 (LRRK2), that are related to various neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and neuropathic pain. We used a combination of machine learning methods including artificial neural networks and advanced multilinear techniques to develop quantitative structure–activity relationship (QSAR) models for all target proteins. The models were applied to screen more than 13,000 natural compounds from a public database to identify active molecules. The best candidate compounds were further confirmed by docking analysis and molecular dynamics simulations using the crystal structures of the proteins. Several compounds with novel scaffolds were predicted that could be used as the basis for development of novel drug inhibitors related to each target

QSPR Modeling of Flash Points: An Update

Quantitative Structure-Property Relationship (QSPR) models for the flash points of 758 organic compounds are developed using geometrical, topological, quantum mechanical and electronic descriptors calculated by CODESSA PRO software. Multilinear regression models link the structures to their reported flash point values. We also report a nonlinear model based on an artificial neural network. The results are discussed in the light of the main factors that influence the property under investigation and its modeling

Novel Carboxamides as Potential Mosquito Repellents

A model was developed using 167 carboxamide derivatives, from the United States Department of Agriculture archival database, that were tested as arthropod repellents over the past 60 yr. An artificial neural network employing CODESSA PRO descriptors was used to construct a quantitative structure-activity relationship model for prediction of novel mosquito repellents. By correlating the structure of these carboxamides with complete protection time, a measure of repellency based on duration, 34 carboxamides were predicted as candidate mosquito repellents. There were four additional compounds selected on the basis of their structural similarity to those predicted. The compounds were synthesized either by reaction of 1-acylbenzotriazoles with secondary amines or by reaction of acid chlorides with secondary amines in the presence of sodium hydride. The biological efficacy was assessed by duration of repellency on cloth at two dosages (25 and 2.5 mol/cm2) and by the minimum effective dosage to prevent Aedes aegypti (L.) (Diptera: Culicidae) bites. One compound, (E)-N-cyclohexyl-N-ethyl-2-hexenamide, was superior to N,N-diethyl-3-methylbenzamide (deet) at both the high dosage (22 d versus 7 d for deet) and low dosage (5 d versus 2.5 d for deet). Only one of the carboxamides, hexahydro-1-(1-oxohexyl)-1H-azepine, had a minimum effective dosage that was equivalent or slightly better than that of deet (0.033µmol/cm2 versus 0.047 µmol/cm2)

Quantitative Structure-Property Relationship Studies on Ostwald Solubility and Partition Coefficients of Organic Solutes in Ionic Liquids

Article discussing quantitative structure-property relationship studies on Ostwald solubility and partition coefficients of organic solutes in ionic liquids