Using molecular quantum similarity measures as descriptors in quantitative structure-toxicity relationships

In this paper molecular quantum similarity measures (MQSM) are used to describe molecular toxicity and to construct Quantitative Structure-Toxicity Relationships (QSTR) models. This study continues the recently described relationships between MQSM and log P values, which permits to use the theoretical MQSM as an alternative to the empirical hydrophobic parameter in QSPR studies. In addition a new type of MQSM is presented in this work: it is based on the expectation value of electron-electron repulsion energy. The molecular properties studied here, as application examples are aquatic toxicity, toxicology on Bacteria and inhibition of a macromolecule employing four different molecular sets.     

Quantum similarity superposition algorithm (QSSA): a consistent scheme for molecular alignment and molecular similarity based on quantum chemistry

The use of the molecular quantum similarity overlap measure for molecular alignment is investigated. A new algorithm is presented, the quantum similarity superposition algorithm (QSSA), expressing the relative positions of two molecules in terms of mutual translation in three Cartesian directions and three Euler angles. The quantum similarity overlap is then used to optimize the mutual positions of the molecules. A comparison is made with TGSA, a topogeometrical approach, and the influence of differences on molecular clustering is discussed.     

About the prediction of molecular properties using the fundamental Quantum QSPR (QQSPR) equation

The present theoretical study analyses the Quantum QSPR fundamental linear equation predictive power. Two main alternative algorithms, among several possible choices, are fully described in an add one and add many basis, while the other possibilities are only sketched out. It is shown that one can also apply the described Quantum QSPR prediction algorithms to parent problems in the framework of empirical QSPR, based on the molecular space framework.     

Exploration of quantitative structure-property relationships (QSPR) for the design of new guanidinium ionic liquids

Computer-aided design of new guanidinium salts was explored and experimentally tested, en route to the discovery of new ionic liquids. Quantitative structure-property relationships were established to predict the mp of guanidinium salts of four different anionic families (Cl⁻, BPh₄⁻, Br⁻, and I⁻). Models were built with a data set of 101 salts and counterpropagation neural networks. Predictions for an independent test set were obtained with R²=0.815, and a fivefold cross-validation procedure yielded R²=0.742. Assisted by the models, six new guanidinium salts were prepared, and the measured melting properties were reasonably in accordance with the predictions. One of the new chloride salts is liquid at room temperature, and three tetraphenylborate salts have mp values lower than those previously available in the data set for that anion.     

Quantum similarity matrices column set as holograms of DF molecular point clouds

A simple argument proves that quantum similarity measures, performed on any density function (DF) tag set of a given quantum object set, create a hologram of such DF tag set.     

Predicting toxicity of benzene derivatives by molecular hologram derived quantitative structure-activity relationships (QSARS)

Holographic quantitative structure-activity relationship (HQSAR) is an emerging QSAR technique with the combined application of molecular hologram, which encodes the frequency of occurrence of various molecular fragment types, and the subsequent partial least squares (PLS) regression analysis. Based on molecular hologram, alignment-free QSAR models could be rapidly and easily developed with highly statistical significance and predictive ability. In this paper, the toxicity data for a series of 83 benzene derivatives to the autotrophic Chlorella vulgaris (IGC50, negative logarithmic form of 6-h 50% population growth inhibition concentration in mmol/l) were subjected to HQSAR analysis and this resulted in a model with a high predictive ability. The robustness and predictive ability of the model were validated by "leave-one-out" (LOO) cross-validation procedure and an external testing set. The influence of fragment distinction parameters and fragment size on the quality of the HQSAR model have been also discussed.     

Evaluation of a quantitative structure-property relationship (QSPR) for predicting mid-visible refractive index of secondary organic aerosol (SOA)

In this work we describe and evaluate a simple scheme by which the refractive index (λ = 589 nm) of non-absorbing components common to secondary organic aerosols (SOA) may be predicted from molecular formula and density (g cm(-3)). The QSPR approach described is based on three parameters linked to refractive index-molecular polarizability, the ratio of mass density to molecular weight, and degree of unsaturation. After computing these quantities for a training set of 111 compounds common to atmospheric aerosols, multi-linear regression analysis was conducted to establish a quantitative relationship between the parameters and accepted value of refractive index. The resulting quantitative relationship can often estimate refractive index to ±0.01 when averaged across a variety of compound classes. A notable exception is for alcohols for which the model consistently underestimates refractive index. Homogenous internal mixtures can conceivably be addressed through use of either the volume or mole fraction mixing rules commonly used in the aerosol community. Predicted refractive indices reconstructed from chemical composition data presented in the literature generally agree with previous reports of SOA refractive index. Additionally, the predicted refractive indices lie near measured values we report for λ = 532 nm for SOA generated from vapors of α-pinene (R.I. 1.49-1.51) and toluene (R.I. 1.49-1.50). We envision the QSPR method may find use in reconstructing optical scattering of organic aerosols if mass composition data is known. Alternatively, the method described could be incorporated into in models of organic aerosol formation/phase partitioning to better constrain organic aerosol optical properties.     

Theoretical advances on coefficients of relational agreement: application to cheminformatics as k‐way biomolecular similarity measures

We provide formal proofs on the partial ordering among chance‐corrected bivariate coefficients of relational agreement. Moreover, we prove that the non‐corrected (chance‐corrected) general formula of multivariate relational agreement is the weighted average of the corresponding non‐corrected (chance‐corrected) general formula of bivariate relational agreement, thus allowing to obtain a specific relationship between each multivariate coefficient and its corresponding bivariate coefficient for seven metric scales of measurements (absolute, difference, ratio, interval, log‐ratio, log‐interval, and ordinal). As a consequence, we report seven newly multivariate coefficients in the literature. Afterwards, eight multivariate coefficients are applied as k‐way biomolecular similarity relations to cheminformatics in order to show their usefulness for discriminating between active and inactive biomolecules. The integration of this type of coefficients into operative virtual screening tools and the generalization to higher‐degree polynomial relationships are discussed in the last part of the paper. Copyright © 2013 John Wiley & Sons, Ltd.     

Orthogonalization of block variables by subspace-projection for quantitative structure property relationship (QSPR) research

A subspace-projection method is developed to construct orthogonal block variable, which is originally from some kinds of series of topological indices or quantum chemical parameters. With the help of canonical correlation analysis, the orthogonal block variables were used to establish the structure-retention index correlation model. The regression of only few new orthogonal variables obtained by canonical correlation analysis against retention index shows significant improvement both in fitting and prediction ability of the correlation model. Moreover, the quantitative intercorrelation between the different block variables of topological indices can also be evaluated with the help of the subspace-projection technique proposed in this work.