Facet diagrams for quantum similarity data

Molecular quantum similarity analysis of estrogenic activity

The main objective of this study was to evaluate the capability of 120 aromatic chemicals to bind to the human alpha estrogen receptor (hER alpha) by the use of quantum similarity methods. The experimental data were segregated into two categories, i.e., those compounds with and without estrogenicity activity (active and inactive). To identify potential ligands, semiquantitative structure-activity relationships were developed for the complete set correlating the presence or lack of binding affinity to the estrogen receptor with structural features of the molecules. The structure-activity relationships were based upon molecular similarity indices, which implicitly contain information related to changes in the electron distributions of the molecules, along with indicator variables, accounting for several structural features. In addition, the whole set was split into several chemical classes for modeling purposes. Models were validated by dividing the complete set into several training and test sets to allow for external predictions to be made.

Molecular quantum similarity-based QSARs for binding affinities of several steroid sets

The application of Molecular Quantum Similarity Measures (MQSM) to correlate biological activities for three different sets of steroids is reported. A general protocol for the generation of descriptors is detailed, thus covering molecular superposition, electronic density fitting, and quantum similarity calculation issues. Satisfactory Quantitative Structure-Activity Relationship (QSAR) models (r(2) in [0.69,0.94] and q(2) in [0.59,0.73]), comparable to previous studies, are obtained in all cases, where steroid binding affinities to different enzymes are studied. In this work, MQSM, properly scaled using Carbó Index, are related to activity using a Partial Least Squares routine.

Using molecular quantum similarity measures under stochastic transformation to describe physical properties of molecular systems

The application of molecular quantum similarity measures (MQSM) to correlate physicochemical properties is reported. Satisfactory quantitative structure-property relationship (QSPR) models are obtained for three molecular sets, where boiling points and chromatographic retention times and indices are studied. In this work, MQSM are scaled using a stochastic transformation and related to molecular properties using the partial least-squares technique.

Antimalarial activity of synthetic 1,2,4-trioxanes and cyclic peroxy ketals, a quantum similarity study

In this work, the antimalarial activity of two series of 20 and 7 synthetic 1,2,4-trioxanes and a set of 20 cyclic peroxy ketals are tested for correlation search by means of Molecular Quantum Similarity Measures (MQSM). QSAR models, dealing with different biological responses (IC90, IC50 and ED90) of the parasite Plasmodium Falciparum, are constructed using MQSM as molecular descriptors and are satisfactorily correlated. The statistical results of the 20 1,2,4-trioxanes are deeply analyzed to elucidate the relevant structural features in the biological activity, revealing the importance of phenyl substitutions.

Identification of active molecular sites using quantum-self-similarity measures

A novel approach to construct theoretical QSAR models is proposed. This technique, based on the systematic use of quantum similarity measures as theoretical molecular descriptors, opens the possibility to localize and to identify the position of the bioactive part of drug molecules in situations, where the nature of the pharmacophore is not known. To test the reliability of this new approach, the method has been applied to the study of steroids binding to corticosteroid-binding human globulin. The studied molecules involved the set of 31 Cramer's steroids, often used as a benchmark set in QSAR studies. It has been shown that theoretical QSAR models based on the present procedure are superior to those derived from alternative existing approaches. In addition, a new method to measure the statistical significance of multiparameter QSAR models is also proposed.

Modeling antimalarial activity: application of Kinetic Energy Density Quantum Similarity Measures as descriptors in QSAR

In this work, is studied the application, within a quantum similarity framework, of the recently described Kinetic Energy Density Function in the evaluation of the antimalarial activity. First, this new type of Density Function is briefly presented from its theoretical foundations, and its inclusion in the molecular quantum similarity is discussed afterward. The application of Kinetic Energy-based Quantum Similarity Measures to QSAR is tested with 2 molecular sets composed of artemisinin derivatives, in which the 50% inhibition of synthesis and reduction of hidrofolate (IC50) in different Plasmodium falciparum clones are analyzed. Satisfactory correlations are obtained for all antimalarial activities in all studied molecular sets. Molecular Quantum Similarity analysis provides a consistent, unbiased, and homogeneous set of molecular descriptors and is a feasible alternative to the use of classical physicochemical descriptors.

Molecular electronic density fitting using elementary Jacobi rotations under atomic shell approximation

Fitted electron density functions constitute an important step in quantum similarity studies. This fact not only is presented in the published papers concerning quantum similarity measures (QSM), but also can be associated with the success of the developed fitting algorithms. As has been demonstrated in previous work, electronic density can be accurately fitted using the atomic shell approximation (ASA). This methodology expresses electron density functions as a linear combination of spherical functions, with the constraint that expansion coefficients must be positive definite, to preserve the statistical meaning of the density function as a probability distribution. Recently, an algorithm based on the elementary Jacobi rotations (EJR) technique was proven as an efficient electron density fitting procedure. In the preceding studies, the EJR algorithm was employed to fit atomic density functions, and subsequently molecular electron density was built in a promolecular way as a simple sum of atomic densities. Following previously established computational developments, in this paper the fitting methodology is applied to molecular systems. Although the promolecular approach is sufficiently accurate for quantum QSPR studies, some molecular properties, such as electrostatic potentials, cannot be described using such a level of approximation. The purpose of the present contribution is to demonstrate that using the promolecular ASA density function as the starting point, it is possible to fit ASA-type functions easily to the ab initio molecular electron density. A comparative study of promolecular and molecular ASA density functions for a large set of molecules using a fitted 6-311G atomic basis set is presented, and some application examples are also discussed.

Quantification of the influence of single-point mutations on haloalkane dehalogenase activity: a molecular quantum similarity study

Controlled modifications in certain protein amino acid residues can lead to changes in their function and stability. Amino acid structural features and their relation to these changes were examined by using quantum molecular similarity techniques. The effect of deliberate mutations in position 172 of the haloalkane dehalogenase enzyme, yielding to variations on the dehalogenation of 1,2-dibromoethane, was studied qualitatively and quantitatively using molecular quantum similarity techniques. A valuable classification of the residues according to their effect on activity was obtained by representing the optimal two-dimensional classical scaling solution. In addition, satisfactory quantitative relationships were found, comparable to those attained by previous studies on this same data set using other techniques. Molecular quantum similarity analysis provides a consistent, unbiased, and homogeneous set of molecular descriptors and is a feasible alternative to the use of physicochemical properties.