Predictive QSAR Models Research Articles

Neuronal nicotinic receptor agonists: a multi-approach development of the pharmacophore.

Based on the results obtained with different automated computational approaches as applied to the study of eleven high-affinity agonists of the neuronal nicotine acetylcholine receptor (nAChR), belonging to different chemical classes, new relevant features were detected which complement the existing pharmacophores. Convergent results from DISCO (Distance Comparison), QXP (Quick Explore), Catalyst/HipHop, and MIPSIM (Molecular Interaction Potential Similarity) allowed us to identify and locate, in a well defined spatial arrangement, three geometrically independent key structural features: (i) a positively charged nitrogen atom for ionic or hydrogen bond interactions, (ii) a lone pair of the pyridine nitrogen or a specific lone pair of a carbonyl oxygen, as a hydrogen bond acceptor, and (iii) a centre of a hydrophobic area generally occupied by aliphatic cycles. The pharmacophore presented herein, along with predictive 2D and 3D QSAR models recently developed in our group, could represent valuable computational tools for the design of new nAChR agonists having therapeutical potential.

Distinguishing between natural products and synthetic molecules by descriptor Shannon entropy analysis and binary QSAR calculations.

Molecular descriptors were identified by Shannon entropy analysis that correctly distinguished, in binary QSAR calculations, between naturally occurring molecules and synthetic compounds. The Shannon entropy concept was first used in digital communication theory and has only very recently been applied to descriptor analysis. Binary QSAR methodology was originally developed to correlate structural features and properties of compounds with a binary formulation of biological activity (i.e., active or inactive) and has here been adapted to correlate molecular features with chemical source (i.e., natural or synthetic). We have identified a number of molecular descriptors with significantly different Shannon entropy and/or "entropic separation" in natural and synthetic compound databases. Different combinations of such descriptors and variably distributed structural keys were applied to learning sets consisting of natural and synthetic molecules and used to derive predictive binary QSAR models. These models were then applied to predict the source of compounds in different test sets consisting of randomly collected natural and synthetic molecules, or, alternatively, sets of natural and synthetic molecules with specific biological activities. On average, greater than 80% prediction accuracy was achieved with our best models. For the test case consisting of molecules with specific activities, greater than 90% accuracy was achieved. From our analysis, some chemical features were identified that systematically differ in many naturally occurring versus synthetic molecules.

Electronic eigenvalue (EEVA): a new QSAR/QSPR descriptor for electronic substituent effects based on molecular orbital energies. A QSAR approach to the Ah receptor binding affinity of polychlorinated biphenyls (PCBs), dibenzo- p-dioxins (PCDDs) and dibenzofurans (PCDFs)

A new descriptor of molecular structure for use in the derivation of predictive QSAR and QSPR models, electronic eigenvalue (EEVA), is described. This is a modification of the recently proposed EVA approach, but is based on computationally-derived molecular orbital energies instead of vibrational frequencies. Like EVA, it is also invariant as to the alignment of the structures concerned. Its performance has been tested with respect to the Ah receptor binding of PCBs, PCDDs and PCDFs, and its predictive ability has been clearly demonstrated. In particular, it seems to be suitable for `pure' electronic substituent effects, i.e., for cases in which both hydrophobic and steric factors are of minor importance.

EEVA (Electronic Eigenvalue): A New QSAR/QSPR Descriptor for Electronic Substituent Effects Based on Molecular Orbital Energies

A new descriptor of molecular structure for use in the derivation of predictive QSAR and QSPR models. EEVA, is described. EEVA (Electronic Eigen Value) is a modification of the recently proposed EVA approach, but it is based on computationally derived molecular orbital energies instead of vibrational frequencies. Like EVA, it is also invariant to alignment of the structures concerned. EEVAs performance has been tested in different applications and its predictive ability was clearly demonstrated. In particular, it seems to be suitable for ‘pure’ electronic substituent effects i.e., for cases in which both hydrophobic and steric factors are of minor importance.

Synthesis, pharmacological evaluation, and structure-activity relationship and quantitative structure-activity relationship studies on novel derivatives of 2,4-diamino-6,7-dimethoxyquinazoline alpha1-adrenoceptor antagonists.

A new series of novel piperazine and non-piperazine derivatives of 2, 4-diamino-6,7-dimethoxyquinazoline was synthesized and evaluated for binding affinity toward alpha1-adrenergic and other G-protein-coupled aminergic receptors. The alpha1-adrenoceptor (AR) subtype selectivity was also investigated for the most interesting compounds. Only compound 16 showed moderate selectivity toward the alpha1b-AR subtype. Selected compounds were tested in vivo in a dog model indicating activity on blood pressure and on the lower urinary tract. Compound 10 showed in vivo potency close to that of prazosin. Powerful interpretative and predictive theoretical QSAR models have been obtained. The theoretical descriptors employed in the rationalization of the alpha1-adrenergic binding affinity depict the key features for receptor binding which can be summarized in an electrostatic interaction between the protonated amine function and a primary nucleophilic site of the receptor, complemented by short-range attractive (polar and dispersive) and repulsive (steric) intermolecular interactions. Moreover, on predictive grounds, the ad hoc derived size and shape QSAR model developed in a previous paper (Rastelli, G.; et al. J. Mol. Struct. 1991, 251, 307-318) proved to be successful in predicting nanomolar alpha1-adrenergic binding affinity for compound 28.

Theoretical descriptors in quantitative structure–affinity and selectivity relationship study of potent N4-substituted arylpiperazine 5-HT1 A receptor antagonists

The ability of ad hoc defined size and shape descriptors and theoretical descriptors derived on a single structure to give poweful interpretative and predictive QSAR models has been compared and evaluated with respect to the quality of the pharmacological data available for a series of structurally diverse 5-HT1 A receptor antagonists, displaying selectivity towards the α1-adrenergic receptor.

On the Use of Chemical Function-Based Alignments as Input for 3D-QSAR

A set of 15 highly flexible competitive inhibitors of rat liver squalene epoxidase (EC.1.14.99.7) covering a wide activity range (IC50 = 2 nM−10 μM) has been investigated by three-dimensional quantitative structure−activity relationships (3D-QSAR). Conformational analysis of the ligands was done by a quasirandom sampling approach with sequential poling. The alignment rule has been defined by a chemical function mapping based method. A comparative molecular field analysis (CoMFA) was performed using interaction energy matrices generated within the GRID program. This approach was shown to yield predictive QSAR models.

GA strategy for variable selection in QSAR studies: GAPLS and D-optimal designs for predictive QSAR model

Variable selection is an important task in obtaining an interpretable and predictive model. In general, variable space has a complex landscape with many local solutions and, therefore, classical optimization techniques cannot be applied. The genetic algorithm (GA) is a recently developed optimization technique that has attracted much attention in several scientific fields. In a previous study (K. Hasegawa et al., J. Chem. Inf. Comput. Sci., 37 (1997) 306), we proposed a novel approach (GAPLS; GAbased PLS) in which variables are selected by GA and partial least squares (PLS). The purpose of this study is to examine whether GAPLS works well for a data set with a medium size of variables. The binding affinities of ligands to benzodiazepine/ GABA a receptor were used as a test example. The relationship between 42 physico-chemical parameters at six positions on 57 benzodiazepines (BZs) and their binding affinities has been investigated by GAPLS. The best PLS model with the selected variables was significantly more predictive than the one with all variables, and the structural requirements for the receptor binding affinity could be estimated from the selected variables.

QSARS of mutagens and carcinogens: Two case studies illustrating problems in the construction of models for noncongeneric chemicals

There is a strong motivation to develop QSAR models for toxicity prediction for use in screening, for setting testing priorities, and for reducing reliance on animal testing. Decisions must be made daily by toxicologists in governments and industry to direct limited testing resources to the most urgent public health problems, and to direct the types of chemical synthesis and product development efforts undertaken. This need has motivated attempts to construct general QSAR models (e.g., for rodent carcinogenicity), not tailored to congeneric series of chemicals. These various attempts have provided interesting and important scientific evidence; however, they have also shared a limited overall performance. The goal of this paper is to illustrate, by two unrelated actual examples of QSARs for mutagens and carcinogens, some fundamental problems relative to the application of general QSAR approaches to noncongeneric chemicals. Both examples consider data sets that are noncongeneric in a chemical structure and mechanism of action sense: in the first case, a mean mutagenic potency defined as an average over multiple genetic toxicity endpoints, and, in the second case, the NTP two-sexes, two species rodent carcinogenicity bioassay results for 280 carcinogens and noncarcinogens. The problems encountered with the QSAR analyses of these two cases indicate that a successful approach to the problem of QSAR modeling of noncongeneric data will need to consider the multidimensional nature of the problem in both a chemical and a biological sense. Since different chemical classes represent largely independent action mechanisms, some means for extracting local QSARs for constituent classes will be necessary. Alternatively, a general QSAR derived for a noncongeneric data set will need to be scrutinized and decomposed along chemical class lines in order establish boundaries for application and confidence levels for prediction.

Predictive QSAR models for estimating biodegradation of aromatic compounds

The development of valid structure biodegradation relationships (SBRs) is restricted by the lack of reproducible published data and by the considered endpoint of degradation data. A classification scheme is required for comparative evaluation of degradation data obtained by different test methods. SBRs based on substructure indicators are available for application to most compounds, but the reliability is still uncertain. SBRs based on physico-chemical parameters are only available for a few classes of compounds based on specific test methods. A combination of several SBRs covering the various transformation pathways provides a promising tool for predicting biodegradability. Two models describing biodegradation are introduced.