Contribution assessment of multiparameter optimization descriptors in CNS penetration.

Assessment of the influence of six physicochemical properties used in the multiparameter optimization (MPO) approach for chemical penetration of the blood-brain barrier was carried out by means of application of logistic regression and multiple linear regression, using a data set of 578 diverse chemicals. It was found that use of an aggregation MPO-score descriptor did not give satisfactory results with central nervous system (CNS)/non-CNS classification. Thus an application of the MPO approach for CNS penetration is ambiguous.

Aqueous Drug Solubility: What Do We Measure, Calculate and QSPR Predict?

Detailed critical analysis of publications devoted to QSPR of aqueous solubility is presented in the review with discussion of four types of aqueous solubility (three different thermodynamic solubilities with unknown solute structure, intrinsic solubility, solubility in physiological media at pH=7.4 and kinetic solubility), variety of molecular descriptors (from topological to quantum chemical), traditional statistical and machine learning methods as well as original QSPR models.

Six global and local QSPR models of aqueous solubility at pH = 7.4 based on structural similarity and physicochemical descriptors.

Aqueous solubility at pH = 7.4 is a very important property for medicinal chemists because this is the pH value of physiological media. The present work describes the application of three different methods (support vector machine (SVM), random forest (RF) and multiple linear regression (MLR)) and three local quantitative structure-property relationship (QSPR) models (regression corrected by nearest neighbours (RCNN), arithmetic mean property (AMP) and local regression property (LoReP)) to construct stable QSPRs with clear mechanistic interpretation.

In silico Prediction of Aqueous Solubility: a Comparative Study of Local and Global Predictive Models.

32 Quantitative Structure-Property Relationship (QSPR) models were constructed for prediction of aqueous intrinsic solubility of liquid and crystalline chemicals. Data sets contained 1022 liquid and 2615 crystalline compounds. Multiple Linear Regression (MLR), Support Vector Machine (SVM) and Random Forest (RF) methods were used to construct global models, and k-nearest neighbour (kNN), Arithmetic Mean Property (AMP) and Local Regression Property (LoReP) were used to construct local models.

Calculation of aqueous solubility of crystalline un-ionized organic chemicals and drugs based on structural similarity and physicochemical descriptors.

Solubilities of crystalline organic compounds calculated according to AMP (arithmetic mean property) and LoReP (local one-parameter regression) models based on structural and physicochemical similarities are presented. We used data on water solubility of 2615 compounds in un-ionized form measured at 25±5 °C. The calculation results were compared with the equation based on the experimental data for lipophilicity and melting point.

Physicochemical properties/descriptors governing the solubility and partitioning of chemicals in water-solvent-gas systems. Part 1. Partitioning between octanol and air.

QSPR analyses of a data set containing experimental partition coefficients in the three systems octanol-water, water-gas, and octanol-gas for 98 chemicals have shown that it is possible to calculate any partition coefficient in the system 'gas phase/octanol/water' by three different approaches: (1) from experimental partition coefficients obtained in the corresponding two other subsystems. However, in many cases these data may not be available. Therefore, a solution may be approached (2), a traditional QSPR analysis based on e.

QSPR analysis of the partitioning of vaporous chemicals in a water-gas phase system and the water solubility of liquid and solid chemicals on the basis of fragment and physicochemical similarity and hybot descriptors.

QSPR analyses of the solubility in water of 558 vapors, 786 liquids and 2045 solid organic neutral chemicals and drugs are presented. Simultaneous consideration of H-bond acceptor and donor factors leads to a good description of the solubility of vapors and liquids. A volume-related term was found to have an essential negative contribution to the solubility of liquids.

SLIPPER-2001 -- software for predicting molecular properties on the basis of physicochemical descriptors and structural similarity.

A new approach for predicting the lipophilicity (log P), solubility (log Sw), and oral absorption of drugs in humans (FA) is described. It is based on structural and physicochemical similarity and is realized in the software program SLIPPER-2001. Calculated and experimental values of log P, log Sw, and FA for 42 drugs were used to demonstrate the predictive power of the program.