Quantum Topological Molecular Similarity Research Articles

Quantitative sequence-activity modeling of ACE peptide originated from milk using ACC-QTMS amino acid indices.

Up to now, numerous peptides/hydrolysates derived from casein and whey protein have shown angiotensin-I-converting enzyme (ACE) inhibitory. In this research, quantum topological molecular similarity (QTMS) indices of amino acids were utilized in quantitative sequence-activity modeling (QSAM) to predict the activity of a set of milk-driven peptides with ACE inhibition. Since the derived peptides have not the same number of residues, we overcame this issue by auto cross covariance (ACC) methodology. Then, some QSAMs were built to predict the pIC50 value of ACE peptides derived from Bovine Casein and Whey. The model established an acceptable relationship between the selected variables and the pIC50 of the peptides. To estimate the performance of the developed models, casein and whey proteins from human, goat, bovine and sheep were virtually broken by trypsin and chymotrypsin enzymes and the ACE activity of the resultant virtual peptides were predicted and some new ACE peptides were proposed.

Interspecies quantitative structure–toxicity–toxicity (QSTTR) relationship modeling of ionic liquids. Toxicity of ionic liquids to V. fischeri, D. magna and S. vacuolatus

Considering the increasing uses of ionic liquids (ILs) in various industrial processes and chemical engineering operations, a complete assessment of their hazardous profile is essential. In the absence of adequate experimental data, in silico modeling might be helpful in filling data gaps for the toxicity of ILs towards various ecological indicator organisms. Using the rationale of taxonomic relatedness, the development of predictive quantitative structure–toxicity–toxicity relationship (QSTTR) models allows predicting the toxicity of ILs to a particular species using available experimental toxicity data towards a different species. Such studies may employ, along with the available experimental toxicity data to a species, molecular structure features and physicochemical properties of chemicals as independent variables for prediction of the toxicity profile against another closely related species. A few such interspecies toxicity correlation models have been reported in the literature for diverse chemicals in general, but this approach has been rarely applied to the class of ionic liquids. The present study involves the use of IL toxicity data towards the bacteria Vibrio fischeri along with molecular structure derived information or computational descriptors like extended topochemical atom (ETA) indices, quantum topological molecular similarity (QTMS) descriptors and computed lipophilicity measure (logk0) for the interspecies exploration of the toxicity data towards green algae S. vacuolatus and crustacea Daphnia magna, separately. This modeling study has been performed in accordance with the OECD guidelines. Finally, predictions for a true external set have been performed to fill the data gap of toxicity towards daphnids and algae using the Vibrio toxicity data and molecular structure attributes.

Exploring simple, transparent, interpretable and predictive QSAR models for classification and quantitative prediction of rat toxicity of ionic liquids using OECD recommended guidelines

The present study explores the chemical attributes of diverse ionic liquids responsible for their cytotoxicity in a rat leukemia cell line (IPC-81) by developing predictive classification as well as regression-based mathematical models. Simple and interpretable descriptors derived from a two-dimensional representation of the chemical structures along with quantum topological molecular similarity indices have been used for model development, employing unambiguous modeling strategies that strictly obey the guidelines of the Organization for Economic Co-operation and Development (OECD) for quantitative structure–activity relationship (QSAR) analysis. The structure–toxicity relationships that emerged from both classification and regression-based models were in accordance with the findings of some previous studies. The models suggested that the cytotoxicity of ionic liquids is dependent on the cationic surfactant action, long alkyl side chains, cationic lipophilicity as well as aromaticity, the presence of a dialkylamino substituent at the 4-position of the pyridinium nucleus and a bulky anionic moiety. The models have been transparently presented in the form of equations, thus allowing their easy transferability in accordance with the OECD guidelines. The models have also been subjected to rigorous validation tests proving their predictive potential and can hence be used for designing novel and “greener” ionic liquids. The major strength of the present study lies in the use of a diverse and large dataset, use of simple reproducible descriptors and compliance with the OECD norms.

Predictive QSAR modelling of algal toxicity of ionic liquids and its interspecies correlation with Daphnia toxicity.

Predictive toxicology using chemometric tools can be very useful in order to fill the data gaps for ionic liquids (ILs) with limited available experimental toxicity information, in view of their growing industrial uses. Though originally promoted as green chemicals, ILs have now been shown to possess considerable toxicity against different ecological endpoints. Against this background, quantitative structure-activity relationship (QSAR) models have been developed here for the toxicity of ILs against the green algae Scenedesmus vacuolatus using computed descriptors with definite physicochemical meaning. The final models emerged from E-state indices, extended topochemical atom (ETA) indices and quantum topological molecular similarity (QTMS) indices. The developed partial least squares models support the established mechanism of toxicity of ionic liquids in terms of a surfactant action of cations and chaotropic action of anions. The models have been developed within the guidelines of the Organization of Economic Co-operation and Development (OECD) for regulatory QSAR models, and they have been validated both internally and externally using multiple strategies and also tested for applicability domain. A preliminary attempt has also been made, for the first time, to develop interspecies quantitative toxicity-toxicity relationship (QTTR) models for the algal toxicity of ILs with Daphnia toxicity, which should be interesting while predicting toxicity of ILs for an endpoint when the data for the other are available.

Cytotoxicity towards CCO cells of imidazolium ionic liquids with functionalized side chains: Preliminary QSTR modeling using regression and classification based approaches

Within this work we evaluated the cytotoxicity towards the Channel Catfish Ovary (CCO) cell line of some imidazolium-based ionic liquids containing different functionalized and unsaturated side chains. The toxic effects were measured by the reduction of the WST-1 dye after 72h exposure resulting in dose- and structure-dependent toxicities. The obtained data on cytotoxic effects of 14 different imidazolium ionic liquids in CCO cells, expressed as EC50 values, were used in a preliminary quantitative structure–toxicity relationship (QSTR) study employing regression- and classification-based approaches. The toxicity of ILs towards CCO was chiefly related to the shape and hydrophobicity parameters of cations. A significant influence of the quantum topological molecular similarity descriptor ellipticity (ε) of the imine bond was also observed.

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.

Quantitative structure–activity relationship for toxicity of ionic liquids to Daphnia magna: Aromaticity vs. lipophilicity

Water solubility of ionic liquids (ILs) allows their dispersion into aquatic systems and raises concerns on their pollutant potential. Again, lipophilicity can contribute to the toxicity of ILs due to increased ability of the compounds to cross lipoidal bio-membranes. In the present work, we have performed statistical model development for toxicity of a set of ionic liquids to Daphnia magna, a widely accepted model organism for toxicity testing, using computed lipophilicity, atom-type fragment, quantum topological molecular similarity (QTMS) and extended topochemical atom (ETA) descriptors. The models have been developed and validated in accordance with the Organization for Economic Co-operation and Development (OECD) guidelines for quantitative structure–activity relationships (QSARs). The best partial least squares (PLS) model outperforms the previously reported multiple linear regression (MLR) model in statistical quality and predictive ability (R2=0.955, Q2=0.917, Rpred2=0.848). In this work, the ETA descriptors show importance of branching and aromaticity while the QTMS descriptor ellipticity efficiently shows which compounds are influential in the data set, with reference to the model. While obvious importance of lipophilicity is evident from the models, the best model clearly shows the importance of aromaticity suggesting that more lipophilic ILs with less toxicity may be designed by avoiding aromaticity, nitrogen atoms and increasing branching in the cationic structure. The developed quantitative models are in consonance with the recent hypothesis of importance of aromaticity for toxicity of ILs.

PAAQD: Predicting immunogenicity of MHC class I binding peptides using amino acid pairwise contact potentials and quantum topological molecular similarity descriptors

Prediction of peptide immunogenicity is a promising approach for novel vaccine discovery. Conventionally, epitope prediction methods have been developed to accelerate the process of vaccine production by searching for candidate peptides from pathogenic proteins. However, recent studies revealed that peptides with high binding affinity to major histocompatibility complex molecules (MHCs) do not always result in high immunogenicity. Therefore, it is promising to predict the peptide immunogenicity rather than epitopes in order to discover new vaccines more effectively. To this end, we developed a novel T-cell reactivity predictor which we call PAAQD. Nonapeptides were encoded numerically, using combining information of amino acid pairwise contact potentials (AAPPs) and quantum topological molecular similarity (QTMS) descriptors. Encoded data were used in the construction of our classification model. Our numerical experiments suggested that the predictive performance of PAAQD is at least comparable with POPISK, one of the pioneering techniques for T-cell reactivity prediction. Also, our experiment suggested that the first and eighth positions of nonapeptides are the most important for immunogenicity and most of the anchor residues in epitope prediction were not important in T-cell reactivity prediction. The R implementation of PAAQD is available at http://pirun.ku.ac.th/~fsciiok/PAAQD.rar.

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.

QSPR of antioxidant phenolic compounds using quantum chemical descriptors

Accelerated systemic free radical production poses a serious problem to healthy living. Since long, phenolic antioxidants have been studied for their ability to react with these toxic radicals. The present work deals with a series of substituted phenolic derivatives with a wide range of antioxidant property data. Quantitative structure–property relationship models have been developed correlating the antioxidant properties of these molecules with quantum chemical descriptors such as Mulliken charges of the common atoms and quantum topological molecular similarity indices of the common bonds. Models were developed based on the training set compounds, and were subsequently validated externally using the test set molecules. The results infer that substituents having a positive mesomeric effect increase the electron density over the phenolic oxygen and reduce the aromatic delocalisation of the lone pair of electron on the phenolic oxygen, thereby enabling effective interaction of the phenolic proton with the free radicals. Moreover, in addition to the mesomeric effect, the inductive effect of the different substituents also plays a crucial role for maintaining the overall charge distribution on the phenolic nucleus. On the basis of the predictive power and interpretability of the models, they may be further utilised for the design of more potent antioxidant molecules.

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.

Comparative QSAR Studies on Toxicity of Phenol Derivatives Using Quantum Topological Molecular Similarity Indices

Quantitative structure activity relationship (QSAR) analyses using a novel type of electronic descriptors called quantum topological molecular similarity (QTMS) indices were operated to describe and compare the mechanisms of toxicity of phenols toward five different strains (i.e., Tetrahymena pyriformis, L1210 Leukemia, Pseudomonas putida, Raja japonica and Cucumis sativus). The appropriate QSAR models for the toxicity data were obtained separately employing partial least squares (PLS) regression combined with genetic algorithms (GA), as a variable selection method. The resulting QSAR models were used to identify molecular fragments of phenol derivatives whose electronic properties contribute significantly to the observed toxicities. Using this information, it was feasible to discriminate between the mechanisms of action of phenol toxicity to the studied strains. It was found that toxicities of phenols to all strains, except with L1210 Leukemia, are significantly affected by electronic features of the phenolic hydroxyl group (C-O-H). Meanwhile, the resulting models can describe the inductive and resonance effects of substituents on various toxicities.

Prediction of the Basicities of Pyridines in the Gas Phase and in Aqueous Solution

The basicities of 125 pyridine derivatives in the gas phase and in water have been correlated with the electron density properties within the framework of quantum topological molecular similarity (QTMS). We used the theory of quantum chemical topology (QCT) to provide ab initio descriptors that are able to predict pK(b) values. Partial least squares (PLS) and the machine-learning technique Kriging generated validated models. Properties were considered for systems in their neutral and protonated forms. The compounds were divided into a training set, used to develop the models, and a test set, for which the predicted values of the different models were compared with the experimental ones. The results were found to be good for those compounds with substituents in the meta and para positions, whereas the use of Kriging was required to obtain reasonable results when ortho derivatives were included. The basicity was found to be better described in the gas phase than in water. Special attention was paid to external validation.

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.

PKaPrediction from “Quantum Chemical Topology” Descriptors

Knowing the pK(a) of a compound gives insight into many properties relevant to many industries, in particular the pharmaceutical industry during drug development processes. In light of this, we have used the theory of Quantum Chemical Topology (QCT), to provide ab initio descriptors that are able to accurately predict pK(a) values for 228 carboxylic acids. This Quantum Topological Molecular Similarity (QTMS) study involved the comparison of 5 increasingly more expensive levels of theory to conclude that HF/6-31G(d) and B3LYP/6-311+G(2d,p) provided an accurate representation of the compounds studies. We created global and subset models for the carboxylic acids using Partial Least Square (PLS), Support Vector Machines (SVM), and Radial Basis Function Neural Networks (RBFNN). The models were extensively validated using 4-, 7-, and 10-fold cross-validation, with the validation sets selected based on systematic and random sampling. HF/6-31G(d) in conjunction with SVM provided the best statistics when taking into account the large increase in CPU time required to optimize the geometries at the B3LYP/6-311+G(2d,p) level. The SVM models provided an average q(2) value of 0.886 and an RMSE value of 0.293 for all the carboxylic acids, a q(2) of 0.825 and RMSE of 0.378 for the ortho-substituted acids, a q(2) of 0.923 and RMSE of 0.112 for the para- and meta-substituted acids, and a q(2) of 0.906 and RMSE of 0.268 for the aliphatic acids. Our method compares favorably to ACD/Laboratories, VCCLAB, SPARC, and ChemAxon's pK(a) prediction software based of the RMSE calculated by the leave-one-out method.

Density Functional Theory‐based Quantitative Structure Activity Relationship (QSAR) Study of Alkanol and Alkanthiol Derivatives

AbstractThe usefulness of the quantum chemical descriptors and a novel group of descriptors called quantum topological molecular similarity (QTMS) indices, calculated at the level of density functional theory (DFT) using 6‐311++G** basis set for QSAR study of anesthetic drugs was examined. A data set containing 24 alkanol and alkanethiol derivatives with known activity was used. Three types of molecular descriptors including QTMS, chemical and quantum chemical was used to derive a quantitative relationship between the anesthetic potency and structural properties. MLR and GA‐PLS were employed to model the relationships between molecular descriptors and biological activity of molecules. Some multiparametric equations containing three descriptors with appropriate statistical qualities were obtained Also, GA‐PLS regression was used to model the structure‐activity relationships more accurately. The results showed that the results obtained by GA‐PLS are similar to MLR and confirmed that hydroxyl group might be the most important region in anesthetic activity of alkanols and akanethiols.

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.