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In this work a multi-objective quantitative structure-property relationship (QSPR) analysis approach was reported based on the study on three partition properties of 50 aromatic sulfur-containing carboxylates. Here multi-objectives (properties) were taken as a vector for QSPR modeling. The quantitative correlations for partition properties were developed using a genetic algorithm-based variable-selection approach with quantum chemical descriptors derived from AMl-based calculations. With the QSPR models, the aqueous solubility, octanol/water partition coefficients and reversed-phase HPLC capacity factors of sulfur-containing compounds were estimated and predicted. Using GA-based multivariate linear regression with cross-validation procedure, a set of the most promising descriptors was selected from a pool of 28 quantum chemical semi-empirical descriptors, including steric and electronic types, to integrally build QSPR models. The selected molecular descriptors included the net charges on carboxyl group (Qoc), the 2nd power of net charges on nitrogen atoms (Q2N), the net atomic charge on the sulfur atoms (Qs), the van der Waals volume of molecule (V), the most positive net atomic charge on hydrogen atoms (QH) and the measure of polarity and polarizability (π), which were main factors affecting the distribution processes of the compounds under study. The statistically best QSPR models of six descriptors were simultaneously obtained by GA-based linear regression analysis. With the selected descriptors and the QSPR equations, mechanisms of partition action of the Sulfur-containing carboxylates were able to be investigated and interpreted.

Holographic QSAR model is constructed to predict relative binding affinities of estradiol derivatives with lamb uterine estrogen receptor. The method does not require the generation of three-dimensional structure of the compounds. The factors that influence the quality of QSAR model are discussed. The result indicates that the model has good predictability and yields a high q LOO 2 value of 0.897. Compared to other QSAR methods, the HQSAR technique is suitable for use in screening large database of chemicals.

Quantitative structure-property relation ships (QSPR) were developed with the quantum semiempirical descriptors computed by AM1 Hamiltonian in MOPAC7.0 for phenylthio, phenylsulfinyl and phenylsulfonyl esters. Using step wise regression analysis with a cross-validation procedure, the most potent and in formative descriptors were screened out from a group of 19 quantum chemical semi-empirical descriptors, including steric and electronic types, to build QSPR models. Several equations were obtained and used to estimate and predict octanol/water partition coefficients and reversed phase high-performance liquid chromatography (HPLC) capacity factors for a series of 30 similar sulfur-containing compounds. The results indicate that molecular descriptors, including average molecular polarizability (α), energy of the lowest unoccupied molecular orbital (ELUMO), net atomic charge on carbon atoms in the carbonyl (QCO), molecular weight (MW), the most positive atomic charge on hydrogen atom (QH+), and net atomic charge on oxygen atoms in the group-NO2 (QNQ2) are the main factors affecting the octanol/water partition coefficients of the compounds under study. And quantum descriptors, covering the most negative atomic charge on an atom Q−), net atomic charge on carbon atoms in the carboxy (QCOO), molecular weight (MW), heat of formation (HF), the most positive atomic charge on a hydrogen atom (QH+), and total energy (TE), are the main factors affecting HPLC capacity factors of these compounds. Rational mechanisms for the two physico-chemical proper ties of the sulfur-containing carboxylates were discussed and interpreted.

Based on the distance between atoms and the electro-negativity of each atom, a new set of descriptors called the molecular electronegativity edge vector (VMEE) being applied to de scribe the molecular structure of peptide analogues, is proposed only from the primary structure of peptides. Here several quantitative structure activity relationship models are proposed on the biological activity of 58 angiotensin converting enzyme (ACE) inhibitors and of 47 bitter tasting dipeptides by multiple linear regression method. The results show that the novel molecular electronegativity edge vector (VMEE) has both excellent structural selectivity and good activity estimation. Besides, this novel molecular electronegative edge vector, be cause it is easy to calculate, will be useful in structure characterization and activity prediction of biological molecules.

The oxygen atom in the hydroxyl group OH of an alcohol molecule is considered a pseudo-carbon atom, like a carbon atom in alkanes, for our present case of extension from alkanes to alcohols. The ratio of electronegativity of oxygen (ENO) to that of carbon (ENC) is defined as the relative electronegativity, REN = ENO/ENC, of a oxygen atom under the condition of REN being 1 for a carbon atom; similarly, the value of bond length for the bond C−O (BLO) over that of the bond C−C (BLC) is regarded as the relative bond length, RBL = BLO/BLC, of the bond C−O with RBL being equal to 1 for C−C. The molecular distance−edge vector, i.e., vector μ, for alkanes can be extended to describe the molecular structure of alcohols instead of alkanes. A quantitative structure−property relationship (QSPR) equation can be modeled by using multiple linear regression (MLR). The first model, M1, between the modified μ vector and boiling points of 106 alcohols is created with correlation coefficient being R = 0.9951 and root-mean-sq...

The MEDV-13, molecular electronegativity distance vector based on 13 atomic types, has at best 91 descriptors. It is impossible to indirectly use multiple linear regression (MLR) to derive a quanti...

The MEDV-13, molecular electronegativity distance vector based on 13 atomic types, has at best 91 descriptors. It is impossible to indirectly use multiple linear regression (MLR) to derive a quantitative structure-activity relationship (QSAR) model. Although principal component regression (PCR) or partial least-squares regression (PLSR) can be employed to develop a latent QSAR model, it is still difficult how to determine the principal components (PCs) and depict the physical meaning of the PCs. So, a genetic algorithm (GA) is first employed to select an optimal subset of the descriptors from original MEDV-13 descriptor set. Then MLR is utilized to build a QSAR model between the optimal subset and the biological activities of three sets of compounds. For 31 benchmark steroids, a 5-descriptor QSAR model (M1) between the corticosteroid-binding globulin (CBG) binding affinity of the steroids and 5-descriptor subset is developed. The root-mean-square error of estimations (RMSEE) and the correlation coefficient of estimations (r) between the CBG binding affinity (BA) observed and the BA estimated by M1 are 0.422 and 0.9182, respectively. The root-mean-square error of predictions (RMSEP) and the correlation coefficient of predictions (q) between the BA observed and the BA predicted by leave-one-out cross validations are 0.504 and 0.8818, respectively. For 58 dipeptides inhibiting angiotensin-converting enzyme (ACE), a 5-variable QSAR model (M2) between the pIC(50) of peptides and 5-descriptor subset is derived. The M2 has a high quality with RMSEE = 0.339 and r = 0.9398 and RMSEP = 0.370 and q = 0.9280. For 16 indomethacin amides and esters (ImAE) inhibiting cyclooxygenase-2 (COX-2), a 6-variable QSAR model (M3) with RMSEE = 0.079 and r = 0.9839 and RMSEP = 0.151 and q = 0.9413 is built.