Abraham Model Correlations for Triethylene Glycol Solvent Derived from Infinite Dilution Activity Coefficient, Partition Coefficient and Solubility Data Measured at 298.15 K

Effective Parameters on the Partition Coefficient of Guanidine Hydrochloride in the Poly (Ethylene Glycol) +Phosphate +Water System at 298.15 K

Prediction of Partition Coefficients and Permeability of Drug Molecules in Biological Systems with Abraham Model Solute Descriptors Derived from Measured Solubilities and Water-to-Organic Solvent Partition Coefficients

Modern drug testing and design includes experimental in vivo and in vitro measurements, combined with in silico computations that enable prediction of the drug candidate’s ADMET (adsorption, distribution, metabolism, elimination and toxicity) properties in the early stages of drug discovery. Recent estimates place the discovery and development cost of a small drug molecule close to US $1.3 bill...

A New Approach on Estimation of Solubility and n-octanol/water Partition Coefficient for Organohalogen Compounds

The aqueous solubility (logW) and n-octanol/water partition coefficient (logP(OW)) are important properties for pharmacology, toxicology and medicinal chemistry. Based on an understanding of the dissolution process, the frontier orbital interaction model was suggested in the present paper to describe the solvent-solute interactions of organohalogen compounds and a general three-parameter model ...

Predicting water solubility and octanol water partition coefficient for carbon nanotubes based on the chiral vector

Components of the chiral vector of carbon nanotubes have been examined in role of structural descriptors. Two-variable models of water solubility and octanol water partition coefficient calculated with components of the chiral vectors have quite good statistical characteristics. These are in case of the water solubility: n=8, r(2)=0.99998, s=0.05338, F=126,611 (training set); n=8, r(2)=0.9999, ...

Matrix correlations for high-dimensional data: the modified RV-coefficient

MOTIVATION Modern functional genomics generates high-dimensional datasets. It is often convenient to have a single simple number characterizing the relationship between pairs of such high-dimensional datasets in a comprehensive way. Matrix correlations are such numbers and are appealing since they can be interpreted in the same way as Pearson's correlations familiar to biologists. The high-dime...