Quantum Topological Molecular Similarity Descriptors Research Articles

Chemometric modeling of the chromatographic lipophilicity parameter logk0 of ionic liquid cations with ETA and QTMS descriptors

Ionic liquids, though promoted as green solvents, exhibit significant toxicity against various organisms in the ecosystem. The lipophilicity of cations plays a determining role in this toxicity. The experimental lipophilicity values being available for a limited number of cations, we modeled the chromatographically derived lipophilicity parameter logk0 of ionic liquid cations using Extended Topochemical Atom (ETA) descriptors and Quantum Topological Molecular Similarity (QTMS) descriptors. Both types of descriptor were previously found to be important in modeling selected toxicity endpoints of ionic liquids. We have performed both internal and external validation tests and randomization experiments while developing the models. The present study suggests that the ETA and QTMS descriptors are efficient in developing robust and reliable lipophilicity models for cations of ionic liquids. The developed models show that lipophilicity increases with the size of cations, and decreases with their electron-richness and hydrogen bonding propensity. The electronic character of the bond joining the quaternary atom with sp3 hybridized carbon is also important. The computed lipophilicity obtained from the developed model was also applied to the computation of rat toxicity data of a large number of compounds, and gave highly satisfactory results.

New autocorrelation QTMS-based descriptors for use in QSAM of peptides

The autocorrelation analysis of the quantum topological molecular similarity (QTMS) has been used to discover some new indices for amino acids. QTMS descriptors include both physical and topological property molecules and produce a matrix of variables per molecule. To handle cubic array of QTMS descriptor calculated for a list of 20 naturally occurring amino acids, autocorrelation analysis has been performed. The proposed indices were validated by construction of quantitative sequence-activity model for three sets of peptides with different chain sizes. Modeling procedure used genetic algorithm-partial least square as variable selection and regression tools. The obtained models were of better or similar qualities compared with previously reported models for the same data sets. Also, the proposed indices in this article were able to determine active site region of the peptides under study.

Novel amino acids indices based on quantum topological molecular similarity and their application to QSAR study of peptides

A new source of amino acid (AA) indices based on quantum topological molecular similarity (QTMS) descriptors has been proposed for use in QSAR study of peptides. For each bond of the chemical structure of AA, eight electronic properties were calculated using the approaches of bond critical point and theory of atom in molecule. Thus, for each molecule a data matrix of QTMS descriptors (having information from both topology and electronic features) were calculated. Using four different criterion based on principal component analysis of the QTMS data matrices, four different sets of AA indices were generated. The indices were used as the input variables for QSAR study (employing genetic algorithm-partial least squares) of three peptides' data sets, namely, angiotensin-converting enzyme inhibitors, bactericidal peptides and the peptides binding to the HLA-A*0201 molecule. The obtained models had better prediction ability or a comparable one with respect to the previously reported models. In addition, by using the proposed indices and analysis of the variable important in projection, the active site of the peptides which plays a significant role in the biological activity of interest, was identified.

QSAR with quantum topological molecular similarity indices: toxicity of aromatic aldehydes to Tetrahymena pyriformis

Extensive production and utilization of aromatic aldehydes and their derivatives without proper certification is alarming with regard to environmental safety. This concern motivated our construction of predictive quantitative structure–activity relationship (QSAR) models for the toxicity of aldehydes to the ecologically important species Tetrahymena pyriformis. Quantum topological molecular similarity (QTMS) descriptors, along with the lipid-water partition coefficient (log K o/w), were used as predictor variables. The QTMS descriptors were calculated at different levels of theory including AM1, HF/3-21G(d), HF/6-31G(d), B3LYP/6-31 + G(d,p), B3LYP/6-311 + G(2d,p) and MP2/6-311+G(2d,p). The data set of 77 aromatic aldehydes was divided into a training set (n = 58) and a test (n = 19) set, and 58 models were developed using partial least squares (PLS) and genetic partial least squares (G/PLS). We evaluated the overall predictive capacity of the models based on leave-one-out predictions for the training set compounds and model derived predictions for the test set compounds. For both PLS and G/PLS, the models built at the HF/6-31G(d) level show better predictivity (based on overall prediction) than the models developed at any of the other five levels. Further validation was also performed utilizing (process and model) randomization tests. We show that improved predictive QSAR models for aldehydic toxicity to Tetrahymena pyriformis can be generated using QTMS descriptors along with log K o/w.

Quantum Topological QSAR Models based on the MOLMAP Approach

Quantum topological molecular similarity produces a two-dimensional array of descriptors for each molecule while a three-dimensional array is obtained by placing the descriptor data matrices of a set of molecules beside each other. Here, we use the multiway data analysis method called molecular maps (MOLMAP) of atom-level properties in a new way. We transferred the three-dimensional array of quantum topological molecular similarity descriptors into new two-dimensional parameters using Kohonen networks, followed by partial least squares. Six different data sets were analyzed by the proposed procedure, which were previously analyzed (Eur. J. Med. Chem. 2006 41 862) by partial least squares applied to unfolded data. They include: (i) the pK(a) of imidazoles, (ii) the ability of a set of indole derivatives to displace [(3)H] flunitrazepam from binding to bovine cortical membranes, (iii) the inhibitory effect of a set of benzimidazoles on the influenza virus, (iv) the interaction of amides with liver alcohol dehydrogenase, (v) inhibition of carbonic anhydrase by sulfonamides and (vi) the toxicity of a set of chlorophenols. Overall, the results showed better statistical results compared with simple unfolding. Furthermore, variable important in projection plots confirmed previous findings about active centers and even in some cases showed more accurate results.

Predictive QSPR modeling of the acidic dissociation constant (pKa) of phenols in different solvents

AbstractGiven the importance of ionization constant (pKa) of phenols in explaining the mechanism of their toxicity, it is of interest to develop theoretical models for the prediction of pKa values of phenols in different solvent systems. In the present communication, we developed predictive QSPR models for pKa values of substituted phenols in seven different solvent systems such as water, dimethyl sulfoxide (DMSO), methanol, dimethylformamide (DMF), acetonitrile (AN), isopropanol, and tert‐butanol using quantum topological molecular similarity (QTMS) descriptors. The data set was divided into training and test sets, and models were developed using partial least squares (PLS) regression from the training set. The predictive potential of the developed models was assessed by the prediction of pKa values of the test set compounds. Root mean square error of prediction (RMSEP) values were used as objective function for selection of the best models in different solvent systems. Good predictive models were developed in all solvent systems except isopropanol. Considering all seven solvent systems, distance descriptors give consistently good results whereas ellipticity descriptors are of less importance. Moreover, plots of ‘variable importance in the projection’ (VIP) for the best models highlight the importance of the bond connecting the phenolic oxygen to the aromatic ring. This suggests the diagnostic nature of QTMS descriptors in identifying the reaction center in acidic dissociation of phenols. Copyright © 2008 John Wiley & Sons, Ltd.

Exploring Predictive QSAR Models Using Quantum Topological Molecular Similarity (QTMS) Descriptors for Toxicity of Nitroaromatics to Saccharomyces cerevisiae

AbstractIn view of the widespread industrial use of nitroaromatics and their consequent ecotoxicological hazard potential, we constructed predictive Quantitative Structure–Activity Relationship (QSAR) models for the toxicity of nitroaromatics to the ecologically important species Saccharomyces cerevisiae. We used Quantum Topological Molecular Similarity (QTMS) descriptors along with electrophilicity index (ELUMO) and lipid water partition coefficient (log Kow) as predictor variables. The QTMS descriptors were calculated at B3LYP/6‐311+G(2d,p) level of theory. QTMS descriptors were employed to complement the deficiency of ELUMO in setting up predictive QSAR models from the view point of external validation. The dataset was divided into a training set (18 compounds) and test set (six compounds) in a ratio of three to one. Partial Least Square (PLS) models were developed based on the training set compounds. The predictive capacity of the models was assessed by the test compounds. The models were also validated by a randomisation test and leave‐one‐seventh‐out crossvalidation test. The results suggest that Bond Critical Point (BCP) descriptors can develop predictive QSAR models for nitroaromatic toxicity to Saccharomyces cerevisiae when used along with ELUMO and log Kow. The diagnostic potential of QTMS descriptors could also reveal the importance of the nitro group for nitroaromatic toxicity.

Exploring predictive QSAR models for hepatocyte toxicity of phenols using QTMS descriptors

We construct predictive QSAR models for hepatocyte toxicity data of phenols using Quantum Topological Molecular Similarity (QTMS) descriptors along with hydrophobicity (logP) as predictor variables. The QTMS descriptors were calculated at different levels of theory including AM1, HF/3-21G(d), HF/6-31G(d), B3LYP/6-31+G(d,p), B3LYP/6-311+G(2d,p) and MP2/6-311+G(2d,p). The external predictability of the best models at the higher levels of theory is higher than that at the lower levels. Moreover, the best QTMS models are better in external predictability than the PLS models using pK(a) and Hammett sigma(+) along with logP. The current study implies the advantage of quantum chemically derived descriptors over physicochemical (experimentally derived or tabular) electronic descriptors in QSAR studies.

Multi‐way Analysis of Quantum Topological Molecular Similarity Descriptors for Modeling Acidity Constant of Some Phenolic Compounds

Three-way analyses of quantum topological molecular similarity descriptors were used for quantitative structure property relationship modeling of the acidity constant of some phenol derivatives. A three-way data was built for different molecules by constructing a data matrix for each molecule. The matrix was produced by considering different bonds in each molecule and different descriptors in each bond. The three-way models parallel factor analysis and N-way partial least squares, and two-way models including partial least squares were used for modeling structure-acidity relationships. Comparison of the results showed that the three-way arrays produced more predictive models with lower over-fitting. The bilinear partial least square model resulted in a biased estimation of acidity constant of prediction set with average relative error of prediction of 1.87%, whereas that obtained by parallel factor analysis and N-way partial least squares was near to zero (i.e. -0.41 and -0.33, respectively). Additionally, the three-way methods allowed investigating the significant impact of different bonds and different descriptors using leverages of the parallel factor analysis loadings.

Application of quantum topological molecular similarity descriptors in QSPR study of the O‐methylation of substituted phenols

The usefulness of a novel type of electronic descriptors called quantum topological molecular similarity (QTMS) indices for describing the quantitative effects of molecular electronic environments on the O-methylation kinetic of substituted phenols has been investigated. QTMS theory produces for each molecule a matrix of descriptors, containing bond (or structure) information in one dimension and electronic effects in another dimension, instead of other methods producing a vector of descriptors for each molecule. A collection of chemometrics tools including principal component analysis (PCA), partial least squares (PLS), and genetic algorithms (GA) were used to model the structure-kinetic data. PCA separated the bond and descriptor effects, and PLS modeled the effects of these parameters on the rate constant data, and GA selected the most relevant subset of variables. The model performances were validated by both cross-validation and external validation. The results indicated that the proposed models could explain about 95% of variances in the rate constant data. The significant effects of variables on the reaction kinetic were identified by calculating variable important in projection (VIP). It was found that the rate constant of esterification of phenols is highly influenced by the electronic properties of the C2--C1--O--H fragment of the parent molecule. Indeed, the C2--X and C4--X bonds (corresponding to ortho and para substituents) were found as highly influential parameters. All of the eight calculated QTMS indices were found significant however, lambda1, lambda2, lambda3, epsilon, and K(r) were detected as highly influential parameters.

Estimation of pKa using quantum topological molecular similarity descriptors: application to carboxylic acids, anilines and phenols.

The current availability of cheap computer power enables the construction of QSARs from modern ab initio quantum chemical data. Multivariate models for three classes of compounds are developed by means of the quantum topological molecular similarity (QTMS) tool, which incorporates descriptors originating from the "Atoms in Molecules" (AIM) theory. Correlations obtained outperform the Hammett and other traditional parameters. The advantage of QTMS over semiempirical and empirical descriptors is demonstrated by the following r(2)/q(2) values: 0.920/0.891 (acids), 0.974/0.953 (anilines), and 0.952/0.884 (phenols).