Anton J. Hopfinger

Application of Genetic Function Approximation to Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships

The genetic function approximation (GFA) algorithm offers a new approach to the problem of building quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) models. Replacing regression analysis with the GFA algorithm allows the construction of models competitive with, or superior to, standard techniques and makes available additional information not provided by other techniques. Unlike most other analysis algorithms, GFA provides the user with multiple models; the populations of models are created by evolving random initial models using a genetic algorithm. GFA can build models using not only linear polynomials but also higher-order polynomials, splines, and Gaussians. By using splinebased terms, GFA can perform a form of automatic outlier removal and classification. The GFA algorithm has been applied to three published data sets to demonstrate it is an effective tool for doing both QSAR and QSPR.

Predicting Caco-2 cell permeation coefficients of organic molecules using membrane-interaction QSAR analysis.

A methodology termed membrane-interaction QSAR (MI-QSAR) analysis has been developed in order to predict the behavior of organic compounds interacting with the phospholipid-rich regions of biological membranes. One important application of MI-QSAR analysis is to estimate ADME properties including the transport of organic solutes through biological membranes as a computational approach to forecasting drug intestinal absorption. A training set of 30 structurally diverse drugs, whose permeability coefficients across the cellular membranes of Caco-2 cells were measured, was used to construct significant MI-QSAR models of Caco-2 cell permeation. Cellular permeation is found to depend primarily upon aqueous solvation free energy (solubility) of the drug, the extent of drug interaction with a model phospholipid (DMPC) monolayer, and the conformational flexibility of the solute within the model membrane. A test set of eight drugs was used to evaluate the predictivity of the MI-QSAR models. The permeation coefficients of the test set compounds were predicted with the same accuracy as the compounds of the training set.

Findings of the Challenge To Predict Aqueous Solubility

The Solubility Challenge is based upon intrinsic solubility data measured in one laboratory for a set of biologically relevant compounds. More than 100 entries to the Solubility Challenge have been received. In several cases multiple entries came from the same person or group. In addition, more than 5% of the prediction sheet entries were incomplete in that predictions were not reported for all 32 compounds of the prediction set. These incomplete entries are not included in the overall findings given here, but the submitted prediction sheets, like those of all other entries, have been scored and will be returned by email to the contestants along with a copy of this report. Overall, 99 completed entries were scored and are reported here.

In Silico Binary Classification QSAR Models Based on 4D-Fingerprints and MOE Descriptors for Prediction of hERG Blockage

Blockage of the human ether-a-go-go related gene (hERG) potassium ion channel is a major factor related to cardiotoxicity. Hence, drugs binding to this channel have become an important biological end point in side effects screening. A set of 250 structurally diverse compounds screened for hERG activity from the literature was assembled using a set of reliability filters. This data set was used to construct a set of two-state hERG QSAR models. The descriptor pool used to construct the models consisted of 4D-fingerprints generated from the thermodynamic distribution of conformer states available to a molecule, 204 traditional 2D descriptors and 76 3D VolSurf-like descriptors computed using the Molecular Operating Environment (MOE) software. One model is a continuous partial least-squares (PLS) QSAR hERG binding model. Another related model is an optimized binary classification QSAR model that classifies compounds as active or inactive. This binary model achieves 91% accuracy over a large range of molecular diversity spanning the training set. Two external test sets were constructed. One test set is the condensed PubChem bioassay database containing 876 compounds, and the other test set consists of 106 additional compounds found in the literature. Both of the test sets were used to validate the binary QSAR model. The binary QSAR model permits a structural interpretation of possible sources for hERG activity. In particular, the presence of a polar negative group at a distance of 6-8 A from a hydrogen bond donor in a compound is predicted to be a quite structure-specific pharmacophore that increases hERG blockage. Since a data set of high chemical diversity was used to construct the binary model, it is applicable for performing general virtual hERG screening.

4D-QSAR: Perspectives in Drug Design

Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. The quantitative structure–activity relationship (QSAR) formalisms are among the most important strategies that can be applied for the successful design new molecules. This review provides a comprehensive review on the evolution and current status of 4D-QSAR, highlighting present challenges and new opportunities in drug design.

Prediction of Ligand−Receptor Binding Thermodynamics by Free Energy Force Field (FEFF) 3D-QSAR Analysis: Application to a Set of Peptidometic Renin Inhibitors

A methodology is presented and applied in which the accurate estimation of ligand-receptor binding thermodynamics is achieved by formulating the calculation as a QSAR problem. When the receptor geometry is known, the free energy force field (FEFF) ligand-receptor binding energy terms can be calculated and used as independent variables in constructing FEFF 3D-QSARs. The FEFF 3D-QSAR analysis of a series of transition state inhibitors of renin was carried out. From a statistical analysis of the free energy contributions to the binding process, FEFF 3D-QSARs were constructed that reveal the change in solvation free energy upon binding and the intramolecular vacuum internal energy of the ligand in the unbound state are the most significant FEFF terms in determining the binding free energy, delta G. Other terms, such as ligand stretching, bending, and torsion energy changes, the intermolecular van der Waals interaction energy, and change in ligand conformational entropy upon binding, are also found to make significant contributions in some FEFF 3D-QSAR delta G models and in delta H and delta S binding models. Overall, a relatively small number of the thermodynamic contributions to the ligand-receptor binding process dominates the thermodynamics of binding in a given model.

Identification of Possible Sources of Nanotoxicity from Carbon Nanotubes Inserted into Membrane Bilayers Using Membrane Interaction Quantitative Structure−Activity Relationship Analysis

Four possible sources of cellular toxicity due to the insertion of a carbon nanotube into a dimyristoylphosphatidylcholine (DMPC) membrane bilayer were explored using the membrane interaction quantitative structure-activity relationship methodology. Comparisons of (i) the structural organization of the membrane bilayer, (ii) dynamical features of the membrane bilayer, and (iii) transport of small polar molecules across the membrane bilayer were carried out with, and without, a carbon nanotube inserted into the bilayer. A fourth study was performed to determine how the transport of solvated ions through the inserted nanotube might alter the structure of the membrane bilayer. Two large changes in the bilayer occur due to insertion of the carbon nanotube. First, there is an alteration in the packing of the DMPC bilayer molecules, which extends at least 18 A from the nanotube, and includes the creation of a relatively open, unoccupied cylindrical ring of 2-4 A thickness directly around the nanotube. Second, the same bilayer structure, which undergoes the change in structural organization, also becomes much more rigid than when the nanotube is not inserted. Solvated calcium ions are predicted to preferentially transport through the inserted nanotube as compared to hydrated sodium ions, but the solvated calcium ion also produces an alteration in the local bilayer structure as it passes through the nanotube. The total diffusion coefficient of ethanol through the membrane bilayer increases by about 35% in the presence of the inserted nanotube. Urea and caffeine also undergo increases in their diffusion coefficients for transport through the bilayer, due to the inserted nanotube, but these increases are less than that of ethanol. Each of the three penetrants also diffuses more directly through the membrane bilayer in the presence of the nanotube, especially caffeine and urea.

Estimation of molecular similarity based on 4D-QSAR analysis: formalism and validation.

The 4D-QSAR paradigm has been used to develop a formalism to estimate molecular similarity measures as a function of conformation, alignment, and atom type. It is possible to estimate the molecular similarity of pairs of molecules based upon the entire ensemble of conformational states each molecule can adopt at a given temperature, normally room temperature. Molecular similarity can be measured in terms of the types of atoms composing each molecule leading to multiple measures of molecular similarity. Multiple measures of molecular similarity can also arise from using different alignment rules to perform relative molecular similarity, RMS, analysis. An alignment independent method of determining molecular similarity measures, referred to as absolute molecular similarity, AMS, analysis has been developed. Various sets and libraries of compounds, including the amino acids, are analyzed using 4D-QSAR molecular similarity analysis to demonstrate the features of the formalism. Exploration of molecular similarity as a function of chirality, identification of common embedded 3D pharmacophores in compounds, and elucidation of 3D-isosteric compounds from structurally diverse libraries are carried out in the application studies.

Quantitative Structure-Based Design: Formalism and Application of Receptor-Dependent RD-4D-QSAR Analysis to a Set of Glucose Analogue Inhibitors of Glycogen Phosphorylase

A method for performing quantitative structure-based design has been developed by extending the current receptor-independent RI-4D-QSAR methodology to include receptor geometry. The resultant receptor-dependent RD-4D-QSAR approach employs a novel receptor-pruning technique to permit effective processing of ligands with the lining of the binding site wrapped about them. Data reduction, QSAR model construction, and identification of possible pharmacophore sites are achieved by a three-step statistical analysis consisting of genetic algorithm optimization followed by backward elimination multidimensional regression and ending with another genetic algorithm optimization. The RD-4D-QSAR method is applied to a series of glucose inhibitors of glycogen phosphorylase b, GPb. The statistical quality of the best RI- and RD-4D-QSAR models are about the same. However, the predictivity of the RD- model is quite superior to that of the RI-4D-QSAR model for a test set. The superior predictive performance of the RD- model is due to its dependence on receptor geometry. There is a unique induced-fit between each inhibitor and the GPb binding site. This induced-fit results in the side chain of Asn-284 serving as both a hydrogen bond acceptor and donor site depending upon inhibitor structure. The RD-4D-QSAR model strongly suggests that quantitative structure-based design cannot be successful unless the receptor is allowed to be completely flexible.

4D-fingerprints, universal QSAR and QSPR descriptors.

An elusive goal in the field of chemoinformatics and molecular modeling has been the generation of a set of descriptors that, once calculated for a molecule, may be used in a wide variety of applications. Since such universal descriptors are generated free from external constraints, they are inherently independent of the data set in which they are employed. The realization of a set of universal descriptors would significantly streamline such chemoinformatics tasks as virtual high-throughout screening (VHTS) and toxicity profiling. The current study reports the derivation and validation of a potential set of universal descriptors, referred to as the 4D-fingerprints. The 4D-fingerprints are derived from the 4D-molecular similarity analysis. To evaluate the applicability of the 4D-fingerprints as universal descriptors, they are used to generate descriptive QSAR models for 5 independent training sets. Each of the training sets has been analyzed previously by several varying QSAR methods, and the results of the models generated using the 4D-fingerprints are compared to the results of the previous QSAR analyses. It was found that the models generated using the 4D-fingerprints are comparable in quality, based on statistical measures of fit and test set prediction, to the previously reported models for the other QSAR methods. This finding is particularly significant considering the 4D-fingerprints are generated independent of external constraints such as alignment, while the QSAR methods used for comparison all require an alignment analysis.