Quantitative molecular similarity analysis (QMSA) methods for property estimation: a comparison of property-based,arbitrary, and tailored similarity spaces

Three classes of arbitrary quantitative molecular similarity analysis (QMSA) methods have been computed using atom pairs, topological indices, and physicochemical properties. Tailored QMSA models have been developed using a selected number of TIs chosen by ridge regression. The methods have been applied to the K-nearest neighbor based estimation of log P of two sets of chemicals. Results show that the property-based and tailored QMSA methods are superior to the arbitrary similarity methods in estimating log P of both sets of chemicals     

Optimal neighbor selection in molecular similarity: comparison of arbitrary versus tailored prediction spaces

Three classes of arbitrary quantitative molecular similarity analysis (QMSA) methods have been computed using atom pairs (APs), topological indices (TIs), and principal components (PCs) derived from topological indices. Tailored QMSA models have been developed from TIs selected through ridge regression. K-nearest neighbor (kNN) based estimation has been applied to all of the methods to estimate normal vapor pressure (p(vap)) and water solubility (sol) for a set of 194 chemicals. Results show that the tailored QMSA methods are superior to arbitrary similarity methods in estimating both of these properties for the given set of chemicals.     

Molecular Similarity and Risk Assessment: Analog Selection and Property Estimation Using Graph Invariants

Abstract

Four molecular similarity measures have been used to select the nearest neighbor of chemicals in two data sets of 139 hydrocarbons and 15 nitrosamines, respectively. The similarity methods are based on calculated graph invariants which include atom pairs, connectivity indices and information theoretic topological indices. The property of the selected nearest neighbor by each method was taken as the estimate of the property under investigation. The results show that for these data sets, all four methods give reasonable estimates of the properties studied.     

Evaluation of similarity measures for searching the dictionary of natural products database

Similarity searches using combinations of seven different similarity coefficients and six different representations have been carried out on the Dictionary of Natural Products database. The objective was to discover if any special methods of searching apply to this database, which is very different in nature from the many synthetic databases that have been the subject of previous studies of similarity searching. Search effectiveness was assessed by a recall analysis of the search outputs from sets of pharmacologically active target structures. The different target sets produce exceptional but contradictory results for the Russell-Rao and Forbes coefficients, which have been shown to be due to a dependence on molecular size; these are the coefficients of choice in the case of large and small structures, respectively. Rankings from these results have been combined using a data fusion scheme and some small gains in performance were normally obtained by using substructural fingerprints and molecular holograms in combination with the Squared Euclidean or Tanimoto coefficients.     

Molecular Lipophilicity Calculations of Chemically Heterogeneous Chemicals and Drugs on the Basis of Structural Similarity and Physicochemical Parameters

Abstract

QSARs based on molecular polarizability (α) and H-bond acceptor factors (∑C_a) as independent variables provided good predictability of octanol/water partition coefficients (P) for chemicals and drugs. However, for some molecules containing few functional groups, the calculated values deviated significantly from those observed. This approach gave good results when applied to a set of 138 chemicals and drugs previously studied by Mannhold and Dross who compared other methods to calculate log P values.

At the same time, three variations on a molecular similarity approach were pursued. In this study, a large training set with experimentally determined octanol/water partition coefficients (P) was searched for structures closely related to the compound-of-interest. The most successful of these variations took the mean log P value of few most closely related compounds after each was adjusted for differences between their and the compound-of-interest's polarizabilities (α) and H-bond acceptor capacities (∑C_a).     

Communications on quantum similarity,part 3: A geometric‐quantum similarity molecular superposition algorithm

This work describes a new procedure to obtain optimal molecular superposition based on quantum similarity (QS): the geometric‐quantum similarity molecular superposition (GQSMS) algorithm. It has been inspired by the QS Aufbau principle, already described in a previous work, to build up coherently quantum similarity matrices (QSMs). The cornerstone of the present superposition technique relies upon the fact that quantum similarity integrals (QSIs), defined using a GTO basis set, depend on the squared intermolecular atomic distances. The resulting QSM structure, constructed under the GQSMS algorithm, becomes not only optimal in terms of its QSI elements but can also be arranged to produce a positive definite matrix global structure. Kruskal minimum spanning trees are also discussed as a device to order molecular sets described in turn by means of QSM. Besides the main subject of this work, focused on MS and QS, other practical considerations are also included in this study: essentially the use of elementary Jacobi rotations as QSM refinement tools and inward functions as QSM scaling methods. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2011     

Performance of theoretical methods and basis sets on the molecular structure, atomisation and ionisation energies, electron affinity, and vibrational spectrum of silylene

This work compares the performance of theoretical methods and basis sets on the molecular structure, atomisation and ionisation energies, electron affinity, and vibrational spectrum of silylene. Silylene, its cation and anion have been studied in ¹ A ₁, ² A ₁ and ² B ₁ states, respectively, in the gas phase and C_2v symmetry. The methods considered are second-order Møller-Plesset perturbation theory (MP2), the density functional theory (DFT), Gaussian-2 (G2) and complete basis set methods (CBS-4M and CBS-Q). The basis sets used are 6-31G(d,p), 6-311G(d,p), 6-31++G(d,p) and 6-311++G(d,p). The functional used for the DFT method is B3LYP. Silylene and its cation and anion have been optimised using the MP2 and DFT methods and the named basis sets. Single-point energy calculations (G2, CBS-4M and CBS-Q) were performed using MP2/6-311++G(d,p) structures and these energies have been used to calculate atomisation energy, ionisation energy and adiabatic electron affinity. Frequency calculations were also done and the raw vibrational frequencies were assigned. It is interesting to note the close similarity between the predicted parameters and some of the available literature values. The results obtained are consistent and converge with different basis sets with improved size and quality. However, the parameters obtained are very much method dependent.     

Predicting hexadecane–air equilibrium partition coefficients (L) using a group contribution approach constructed from high quality data

A group contribution-based quantitative structure–property relationship (QSPR) for the hexadecane–air equilibrium partition coefficients (L) of organic chemicals is developed using the iterative fragment selection (IFS) approach. This new QSPR includes in its training and external validation data sets L values for a large number of structurally complex chemicals measured by the same group using consistent methods. The resulting QSPR has better predictive power than other prediction methods trained primarily using data for chemicals of simpler structures, and measurements of L values from diverse sources. For a subset of chemicals in which the L values have non-additive effects caused by intramolecular hydrogen bonds, the new QSPR gives much better performance in comparison to the most commonly used prediction method.     

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.     

Fugacity Calculations Using Estimated Physicochemical Properties

Abstract

Fugacity calculations according to Mackay and Paterson have been performed for a set of 114 chemicals. Calculations using experimental input parameters and calculations based on estimated parameters are comparatively presented. It is shown that estimated parameters are useful for an estimation of environmental distribution provided the estimation methods are chosen carefully and experimental melting points as well as boiling points are available. Estimation methods for vapour pressure and water solubility need further development.     

Toward similarity measures for macromolecular bodies: Medla test calculations for substituted benzene systems

A new method is proposed for the evaluation of numerical similarity measures for large molecules, defined in terms of their electron density (ED) distributions. The technique is based on the Molecular Electron Density Lego Assembler (MEDLA) approach, proposed earlier for the generation of ab initio quality electron densities for proteins and other macromolecules. The reliability of the approach is tested using a family of 13 substituted aromatic systems for which both standard ab initio electron density computations and the MEDLA technique are applicable. These tests also provide additional examples for evaluating the accuracy of the MEDLA technique. Electron densities for a series of 13 substituted benzenes were calculated using the standard ab initio method with STO-3G, 3-21G, and 6-31G** basis sets as well as the MEDLA approach with a 6-31G** database of electron density fragments. For each type of calculation, pairwise similarity measures of these compounds were calculated using a point-by-point numerical comparison of the EDs. From these results, 2D similarity maps were constructed, serving as an aid for quick visual comparisons for the entire molecular family. The MEDLA approach is shown to give virtually equivalent numerical similarity measures and similarity maps as the standard ab initio method using a 6-31G** basis set. By contrast, significant differences are found between the standard ab initio 6-31G** results and the standard ab initio results obtained with smaller STO-3G and 3-21G basis sets. These tests indicate that the MEDLA-based similarity measures faithfully mimic the actual, standard ab initio 6-31G** similarity measures, suggesting the MEDLA method as a reliable technique to assess the shape similarities of proteins and other macromolecules. The speed of the MEDLA computations allows rapid, pairwise comparisons of the actual EDs for a series of molecules, requiring no more computer time than other simplified, less detailed representations of molecular shape. The MEDLA method also reduces the need to store large volumes of numerical density data on disk, as these densities can be quickly recomputed when needed. For these reasons, the proposed MEDLA similarity analysis technique is likely to become a useful tool in computational drug design. © 1995 John Wiley & Sons, Inc.