ABSTRACT
We present an extended QSPR modeling of solubilities of about 500 substances in series of up to 69 diverse solvents. The models are obtained with our new software package, CODESSA PRO, which is furnished with an advanced variable selection procedure and a large pool of theoretically derived molecular descriptors. The squared correlation coefficients and squared standard deviations (variances) range from 0.837 and 0.1 for 2-pyrrolidone to 0.998 and 0.02 for dipropyl ether, respectively. The predictive power of the models was verified by using the "leave-one-out" cross-validation procedure. The QSPR models presented are suitable for the rapid evaluation of solvation free energies of organic compounds.
ABSTRACT
As part of our general QSPR treatment of solubility (started in the preceding paper), we now present quantitative relationships between solvent structures and the solvation free energies of individual solutes. Solvation free energies of 80 diverse organic solutes are each modeled in a range from 15 to 82 solvents using our CODESSA PRO software. Significant correlations (in terms of squared correlation coefficient) are found for all the 80 solutes: the best fit is obtained for n-propylamine (R(2) = 0.996); the lowest R(2) corresponds to toluene (0.604).
ABSTRACT
Whole-molecule descriptors are obtained computationally from molecular structures using a variety of programs. Their applications are reviewed in the areas of solubility, bioavailability, bio- and nonbio-degradability and toxicity.
Subject(s)
Computer-Aided Design , Drug Design , Quantitative Structure-Activity Relationship , Animals , Combinatorial Chemistry Techniques , Humans , Molecular StructureABSTRACT
A system suitable for tracking the amounts and locations of ca. 10 000 chemicals is described.
ABSTRACT
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 compounds using a quantitative structure-property relationship (QSPR) approach. A seven-descriptor linear correlation with a squared correlation coefficient (R2) of 0.815 is reported for a data set of 521 compounds. Using the sum of ZINDO oscillator strengths in the integration range as an additional descriptor allowed reduction in the number of descriptors producing a robust model for 460 compounds with five descriptors and a squared correlation coefficient 0.857. The descriptors used in the models are discussed with respect to the physical nature of the UV absorption process.
ABSTRACT
The melting points of several imidazolium-based ionic liquids or ionic liquid analogues were correlated using the CODESSA program in order to develop predictive tools for determination of suitable ionic liquid salts. The data set consisted of melting point data (degrees C) for 104 substituted imidazolium bromides divided on the basis of the N-substituents into three subsets: A-57 compounds, B-29 compounds, and C-18 compounds. The 45 benzimidazolium bromides form set D. Five-parameter correlations were obtained for (i) set A with R2 = 0.7442, (ii) set B with R2 = 0.7517, and (iii) set D with R2 = 0.6899, while set C was correlated with a three parameter equation with R(2) = 0.9432. These descriptors for predicting the melting points of the imidazolium and benzimidazolium bromides were based on the size and electrostatic interactions in the cations.
ABSTRACT
In an attempt to develop predictive tools for the determination of new ionic liquid solvents, QSPR models for the melting points of 126 structurally diverse pyridinium bromides in the temperature range 30-200 degrees C were developed with the CODESSA program. Six- and two-descriptor equations with squared correlation coefficients (R(2)) of 0.788 and 0.713, respectively, are reported for the melting temperatures. The models illustrate the importance of information content indices, total entropy, and the average nucleophilic reactivity index for an N atom.
