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

Development of small molecule non-peptide formyl peptide receptor (FPR) ligands and molecular modeling of their recognition

Formyl peptide receptors (FPRs) are G protein-coupled receptors (GPCRs) expressed on a variety of cell types. These receptors play an important role in the regulation of inflammatory reactions and sensing cellular damage. They have also been implicated in the pathogenesis of various diseases, including neurodegenerative diseases, cataract formation, and atherogenesis. Thus, FPR ligands, both agonists and antagonists, may represent novel therapeutics for modulating host defense and innate immunity. A variety of molecules have been identified as receptor subtype-selective and mixed FPR agonists with potential therapeutic value during last decade. This review describes our efforts along with recent advances in the identification, optimization, biological evaluation, and structure-activity relationship (SAR) analysis of small molecule non-peptide FPR agonists and antagonists, including chiral molecules. Questions regarding the interaction at the molecular level of benzimidazoles, pyrazolones, pyridazin-3(2H)-ones, N-phenylureas and other derivatives with FPR1 and FPR2 are discussed. Application of computational models for virtual screening and design of FPR ligands is also considered.

Improving the usefulness of molecular similarity-based chemical prioritization strategies

Quantitative molecular similarity analysis (QMSA) is a seemingly useful tool for estimating environmental properties for the hundreds of emerging contaminants that have not yet been fully evaluated. Moreover, calibrated QMSA models are also useful for prioritizing research among currently unmeasured chemicals of interest. Previous work has demonstrated that prioritization based on molecular 'representativeness', as parameterized using summed Euclidean distances in n dimensions corresponding to n molecular descriptors, improves the prediction accuracy of QMSA models compared to random selection of compounds to be measured. In this study, we use two datasets of environmental parameters (i.e. in vitro oestrogenicity and sorption distribution coefficient Kd ) to demonstrate that maximizing representativeness alone cannot deliver optimal improvement in prediction accuracy if many of the chemicals that have already been measured are themselves highly representative. Thus, proper QMSA-based prioritization among unmeasured chemicals constitutes a balance between maximizing representativeness and minimizing redundancy. It is demonstrated that redundancy considerations are especially critical for highly heterogeneous datasets, and some discussion about achieving a proper balance between the two prioritization criteria is presented.

Computational structure-activity relationship analysis of small-molecule agonists for human formyl peptide receptors

N-formyl peptide receptors (FPRs) are important in host defense. Because of the potential for FPRs as therapeutic targets, recent efforts have focused on identification of non-peptide agonists for two FPR subtypes, FPR1 and FPR2. Given that a number of specific small-molecule agonists have recently been identified, we hypothesized that computational structure-activity relationship (SAR) analysis of these molecules could provide new information regarding molecular features required for activity. We used a training set of 71 compounds, including 10 FPR1-specific agonists, 36 FPR2-specific agonists, and 25 non-active analogs. A sequence of (1) one-way analysis of variance selection, (2) cluster analysis, (3) linear discriminant analysis, and (4) classification tree analysis led to the derivation of SAR rules with high (95.8%) accuracy for correct classification of compounds. These SAR rules revealed key features distinguishing FPR1 versus FPR2 agonists. To verify predictive ability, we evaluated a test set of 17 additional FPR agonists, and found that the majority of these agonists (>94%) were classified correctly as agonists. This study represents the first successful application of classification tree methodology based on atom pairs to SAR analysis of FPR agonists. Importantly, these SAR rules represent a relatively simple classification approach for virtual screening of FPR1/FPR2 agonists.

Structure-activity relationship analysis of N-benzoylpyrazoles for elastase inhibitory activity: a simplified approach using atom pair descriptors

Previously, we utilized high throughput screening of a chemical diversity library to identify potent inhibitors of human neutrophil elastase and found that many of these compounds had N-benzoylpyrazole core structures. We also found individual ring substituents had significant impact on elastase inhibitory activity and compound stability. In the present study, we utilized computational structure-activity relationship (SAR) analysis of a series of 53 N-benzoylpyrazole derivatives to further optimize these lead molecules. We present an improved approach to SAR methodology based on atom pair descriptors in combination with 2-dimensional (2D) molecular descriptors. This approach utilizes the rich representation of chemical structure and leads to SAR analysis that is both accurate and intuitively easy to understand. A sequence of ANOVA, linear discriminant, and binary classification tree analyses of the molecular descriptors led to the derivation of SAR rule-based algorithms. These rules revealed that the main factors influencing elastase inhibitory activity of N-benzoylpyrazole molecules were the presence of methyl groups in the pyrazole moiety and ortho-substituents in the benzoyl radical. Furthermore, our data showed that physicochemical characteristics (energy of frontier molecular orbitals, molar refraction, lipophilicity) were not necessary for achieving good SAR, as comparable quality of SAR classification was obtained with atom pairs and 2D descriptors only. This simplified SAR approach may be useful to qualitative SAR recognition problems in a variety of data sets.

Prediction of tissue: air partition coefficients--theoretical vs. experimental methods

Predictive QSAR models for rat and human tissue : air partition coefficients, namely blood : air, fat : air, brain : air, liver : air, muscle : air, and kidney : air were developed utilizing experimentally determined partition coefficients for 131 chemicals obtained from the literature and molecular descriptors based solely on chemical structure. The descriptors were partitioned into four hierarchical classes, including topostructural, topochemical, 3-dimensional, and ab initio quantum chemical. Three types of regression methodologies--ridge regression, principal components regression, and partial least squares regression--were used comparatively in the development of the structure-based models. In addition to the structure-based models, ordinary least squares regression was used to develop comparative models based on experimentally determined properties including saline : air and olive oil : air partition coefficients. The results of the study indicate that many of the structure-based models are comparable or superior to their respective property-based models. This is an important result considering that structural descriptors can be calculated quickly and inexpensively for both existing chemicals and those not yet synthesized. It was also found that ridge regression outperformed principal components regression and partial least squares regression, with respect to the structure-based models, and that generally the topochemical descriptors alone produced models of good predictive ability.