No Title

Abstract

Several QSPR models were developed for predicting intrinsic aqueous solubility, S(o). A data set of 5,964 neutral compounds was sub-divided into two classes, aromatic and non-aromatic compounds. Three models were created with different methods on both data sets: two regression models (multiple linear regression and partial least squares) and an artificial neural network model. These models were based on 3343 aromatic and 1674 non-aromatic compounds for training sets; 938 compounds were used in external validation testing. The range in -log S(o) is -1.6 to 10. Topological structure descriptors were used with all models. A genetic algorithm was used for descriptor selection for regression models. For the artificial neural network (ANN) model, descriptor selection was done with a backward elimination process. All models performed well with r2 values ranging 0.72 to 0.84 in external validation testing. The mean absolute errors in validation ranged from 0.44 to 0.80 for the classes of compounds for all the models. These statistical results indicate a sound ANN model. Furthermore, in a comparison with eight other available models, based on predictions using a validation test set (442 compounds), the artificial neural network model presented in this work (CSLogWS) was clearly superior based on both the mean absolute error and the percentage of residuals less than one log unit. In the ANN model both E-State and hydrogen E-State descriptors were found to be important.

CE50: Quantifying collision induced dissociation energy for small molecule characterization and identification

“…Thus, although the present model is not useful for predictive purposes, its development clearly suggests that modeling of a larger dataset could be predictive [23,28,39,[41][42][43][44]. Furthermore, the anticipated improved model may also assist in identifying further structural features important in determining precursor ion stability and fragmentation mechanisms [23,28,[42][43][44][45][46]. In addition, our model (eq 7) was created based on the structures of the unprotonated precursors.…”

“…Molconn software [20] was used to calculate a wide range of molecular descriptors for the set of 55 compounds for which we had determined experimental CE 50 values. The set of structure descriptors that were evaluated consisted of E-state descriptors for atoms and bonds [17,[21][22][23], molecular connectivity descriptors representing ring structures and extended paths of atoms [24 -27], and descriptors related to maximum and minimum electron accessibilities on hydrogen atoms and ϪXH n groups in the molecule [23,27,28]. A total of 39 structure descriptors were evaluated for multiple linear regression (MLR) analysis.…”

“…Detailed information explaining the meaning of the descriptors used in the model (eq 7) has been previously published [17,23,28,[41][42][43][44][45][46][47][48]. The descriptor that contributes most (50%) to the computed value of CE 50 is sumI.…”

Antiplasmodial Activity of [(Aryl)arylsulfanylmethyl]Pyridine

“…The logP and pK a values of the AASMPs were calculated by using the property prediction software CSPredict (ChemSilico LLC, Tewksbury, MA) (66,69,70).…”

Predicting Aqueous Solubility of Chlorinated Hydrocarbons by the MCI Approach

“…The first approach [4][5][6][7][8] is to build model from more easily measured physicochemical properties, such as melting point, boiling point, molar volume, partition coefficient, chromatographic retention time, etc. The other method is based on the information from the molecular of the organic chemicals, which can be further divided into two classes, one is group contributions method [9][10][11][12] and the other is QSPR approach [1,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32].…”