Lluís Amat

Quantum similarity measures under atomic shell approximation: First order density fitting using elementary Jacobi rotations

The elementary Jacobi rotations technique is proposed as a useful tool to obtain fitted electronic density functions expressed as linear combinations of atomic spherical shells, with the additional constraint that all coefficients are kept positive. Moreover, a Newton algorithm has been implemented to optimize atomic shell exponents, minimizing the quadratic error integral function between ab initio and fitted electronic density functions. Although the procedure is completely general, as an application example both techniques have been used to compute a 1S‐type Gaussian basis for atoms H through Kr, fitted from a 3‐21G basis set. Subsequently, molecular electronic densities are modeled in a promolecular approximation, as a simple sum of parameterized atomic contributions. This simple molecular approximation has been employed to show, in practice, its usefulness to some computational examples in the field of molecular quantum similarity measures. © 1997 John Wiley & Sons, Inc. J Comput Chem 18: 2023–2039, 1997

Toward a Global Maximization of the Molecular Similarity Function: Superposition of Two Molecules

A quantum similarity measure between two molecules is normally identified with the maximum value of the overlap of the corresponding molecular electron densities. The electron density overlap is a function of the mutual positioning of the compared molecules, requiring the measurement of similarity, a solution of a multiple‐maxima problem. Collapsing the molecular electron densities into the nuclei provides the essential information toward a global maximization of the overlap similarity function, the maximization of which, in this limit case, appears to be related to the so‐called assignment problem. Three levels of approach are then proposed for a global search scanning of the similarity function. In addition, atom—atom similarity Lorentzian potential functions are defined for a rapid completion of the function scanning. Performance is tested among these three levels of simplification and the Monte Carlo and simplex methods. Results reveal the present algorithms as accurate, rapid, and unbiased techniques for density‐based molecular alignments.

Three-dimensional quantitative structure-activity relationships from tuned molecular quantum similarity measures: prediction of the corticosteroid-binding globulin binding affinity for a steroid family.

Predictive models based on tuned molecular quantum similarity measures and their application to obtain quantitative structure-activity relationships (QSAR) are described. In the present paper, the corticosteroid-binding globulin binding affinity of a 31 steroid family is studied by means of a multilinear regression using molecular descriptors derived from mixed steric-electrostatic quantum similarity matrixes as parameters, obtaining satisfactory predictions. A systematic procedure to treat outliers by using triple-density quantum similarity measures is also presented. This method depicts an alternative to the grid-based QSAR techniques, providing a consistent approach that avoids problematic result dependency on the grid parameters.

Fitted electronic density functions from H to Rn for use in quantum similarity measures: cis‐diamminedichloroplatinum(II) complex as an application example

A consistent set of fitted electronic density functions was generated for the elements from hydrogen to radon using an algorithm based on the elementary Jacobi rotations (EJR) technique. The main distinguishing attribute of this fitting procedure is the production of approximated electronic density functions with positive definite expansion coefficients; in this way, the statistical meaning of the probability distribution is preserved. The methodology, which was fully described previously, was modified in this work to improve and accelerate the fitting procedure. This variation concerns the optimization method employed to obtain the optimal angle of the EJR, implementing an algorithm based on a Taylor series expansion. Additionally, a new 1S‐Type Gaussian basis set for atoms H to Rn is presented, that was fitted from a primitive basis set of Huzinaga. Fitted density functions facilitate theoretical calculations over large molecules and may be employed in many areas of computational chemistry, for example, in quantum similarity measures (QSM). To verify the basis set, a sound example related to QSM applications is given. This corresponds to the comparison of experimental structures obtained from X‐ray determination for cis‐diamminedichloroplatinum(II) complex with optimized molecular geometries using several theoretical methods to quantify the differences between the analyzed levels of theory. ©1999 John Wiley & Sons, Inc. J Comput Chem 20: 911–920, 1999

Molecular basis of quantitative structure-properties relationships (QSPR): A quantum similarity approach

Since the dawn of quantitative structure-properties relationships (QSPR), empirical parameters related to structural, electronic and hydrophobic molecular properties have been used as molecular descriptors to determine such relationships. Among all these parameters, Hammett σ constants and the logarithm of the octanol- water partition coefficient, log P, have been massively employed in QSPR studies. In the present paper, a new molecular descriptor, based on quantum similarity measures (QSM), is proposed as a general substitute of these empirical parameters. This work continues previous analyses related to the use of QSM to QSPR, introducing molecular quantum self-similarity measures (MQS-SM) as a single working parameter in some cases. The use of MQS-SM as a molecular descriptor is first confirmed from the correlation with the aforementioned empirical parameters. The Hammett equation has been examined using MQS-SM for a series of substituted carboxylic acids. Then, for a series of aliphatic alcohols and acetic acid esters, log P values have been correlated with the self-similarity measure between density functions in water and octanol of a given molecule. And finally, some examples and applications of MQS-SM to determine QSAR are presented. In all studied cases MQS-SM appeared to be excellent molecular descriptors usable in general QSPR applications of chemical interest.

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.

MOLECULAR QUANTUM SIMILARITY MEASURES TUNED 3D QSAR : AN ANTITUMORAL FAMILY VALIDATION STUDY

In this work, a new methodology to construct a tuned QSAR model is presented, which is based on a convex set formalism. The present procedure continues previous 3D QSAR studies, performed using molecular quantum similarity measures (MQSM). With this new computational tool, the efficiency of MQSM applied to QSAR analysis is significantly improved. A reliable QSAR model is obtained using convex linear combinations of different kinds of MQSM, corresponding to different quantum-mechanical operators related to the quantum similarity integral. The active compounds studied here, as a case study, are a set of antitumor agents, the camptothecin molecule and analogues, and the property evaluated is the topoisomerase-I inhibition activity. Before performing a tuned QSAR analysis with this particular molecular set, a simple QSAR study for all the different possible types of MQSM is carried out. In addition, another application of MQSM is presented, to determine which method can be used to optimize molecular structures in order to reproduce experimental molecular geometries as well as possible.

Molecular quantum similarity measures as an alternative to log P values in QSAR studies

A new molecular descriptor of hydrophobicity based on molecular quantum similarity measures (MQSM), which can be used to replace the log P parameter in QSAR studies, is proposed. Unlike the majority of existing approaches for calculation of log P, the present methodology does not rely on the use of fragment additive contributions, but rather it is based on the comparison of quantum chemically calculated electron density distributions of a given molecule in water and in 1‐octanol, using MQSM. The method has been tested on a broad series of 58 molecules including such structural types as aliphatic hydrocarbons, alcohols, amines, halides, carboxylic acids, esters, amides, and ketones, as well as more complex systems with two functional groups. In all cases investigated, an excellent linear relationship between calculated MQSM and log P values was found. Additionally, an example of QSAR analysis is presented using MQSM instead of log P values, corresponding to predict the narcosis of tadpoles. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 1575–1583, 1998

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 Cramers 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.

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.