David E. Patterson

Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins

Comparative molecular field analysis (CoMFA) is a promising new approach to structure/activity correlation. Its characteristic features are (1) representation of ligand molecules by their steric and electrostatic fields, sampled at the intersections of a three-dimensional lattice, (2) a new ‘field fit” technique, allowing optimal mutual alignment within a series, by minimizing the RMS field differences between molecules, (3) data analysis by partial least squares (PLS), using cross-validation to maximize the likelihood that the results have predictive validity, and (4) graphic representation of results, as contoured three-dimensional coefficient plots. CoMFA is exemplified by analyses of the affinities of 21 varied steroids to corticosteroidand testosterone-binding globulins. Also described are the sensitivities of results to the nature of the field and the definition of the lattice and, for comparison, analyses of the same data using various combinations of other parameters. From these results, a set of ten steroid-binding affinity values unknown to us during the CoMFA analysis were well predicted. A major goal in chemical research is to predict the behavior of new molecules, using relationships derived from analysis of the properties of previously tested molecules. Relationships derived primarily by empirical analysis of a data table, whose columns are numerical property values and whose rows are compounds, usually taking the form of a linear equation, are called quantitative structure/activity relationships (QSAR).I Especially in biological applications, it has long been agreed that the most relevant numerical property values would be shape-dependent. Work on comparative molecular field analysis (CoMFA) began 12 years ago with two additional observations: (1) at the molecular level, the interactions which produce an observed biological effect are usually non-covalent; and ( 2 ) molecular mechanics force fields, most of which treat noncovalent (non-bonded) interactions only as steric and electrostatic forces, can account precisely for a great variety of observed molecular properties.2 Thus it seems reasonable that a suitable sampling of the steric and electrostatic fields surrounding a set of ligand (drug) molecules might provide all the information necessary for understanding their observed biological properties. However, the emergence of a practical CoMFA methodology had to await a new method of data analysis, partial least squares (PLS),3 which can derive robust linear equations from tables having many more columns than rows, and a number of advances in the methodology of molecular graphics. Other “3D-QSAR” methodologies have been described. The molecular shape (MS) approaches, developed independently by Simon et aL4 and by H ~ p f i n g e r , ~ compare net, rather than location-dependent, differences in molecular connectivities, volumes, and/or fields. A second approach, the “distance geometry” method of Crippen,6 provides validation of a ”site-point” hypothesis, a list of binding set coordinates and properties that must be proposed by the investigator. A prototype version of the CoMFA method is called “DYLOMMS”.7 In related work, for exploring binding modes of ligands to receptors, Goodford* advocates the display of probe-interaction “grids”, similar to thme used in CoMFA, while Hansch, Blaney, Langridge, et aL9 have shown the complementarity of QSAR and molecular graphics in understanding enzyme inhibitor data. Below we describe the main features of the CoMFA approach, exemplifying its use by analyzing the binding affinities of 21 varied steroid structures to human corticosteroid-binding globulins (CBG) and testosterone-binding globulins10 (TBG). In this series, the comparative rigidity of the steroid nucleus allows the conformational variable to be neglected, and the in vitro, particularly simple, character of the test system minimizes the importance of nonreceptor-related, hence non-shape-related, compound differences on the experimental observations.” We then investigated the *Author to whom all correspondence should be addressed. 0002-7863/88/15 10-5959

Comparative molecular field analysis (CoMFA). 2. Toward its use with 3D-structural databases

01.50/0 sensitivity of the excellent results obtained to critical model assumptions. For the purpose of comparison, we have also analysed these steroid binding data using both classical and other ”molecular shape” parameters, in various combinations. Finally, toward the end of this work, we were informed of additional corticosteroid binding data,12 and thus were able to test the ability of our model to predict the binding constants of ten more, structurally diverse, steroids. Computational Methods CoMFA Methodology. The overall data flow of a CoMFA analysis appears in Figure I . Its top two panels show how the data table is constructed from the field values at the lattice intersections. These automatically calculated parameters are the energies of steric (van der Waals 6-12) and electrostatic (Coulombic, with a 1 / r dielectric) interaction between the compound of interest, and a “probe atom” placed at the various intersections of a regular three-dimensional lattice, large enough to surround all of the compounds in the series, and with a 2.0 A separation between lattice point unless otherwise stated. The van der Waals A / B values were taken from the standard Tripos force field” and the atomic charges were calculated by the method of Gasteiger and Mar~i l i . ’~ Unless stated otherwise, the probe atom had the van der Waals properties of sp3 carbon and a charge of +1.0. Wherever the prove atom experiences a steric repulsion greater than “cutoff“ (30 kcal/mol ( I ) Martin, Y. C. Quantitative Drug Design; Marcel Dekker: New York, 1978. (2) Burkert, U.; Allinger, N. L. Molecular Mechanics; American Chemical Society: Washington, DC, 1982. (3) Wold, S . ; Ruhe, A,; Wold, H.; Dunn, W. J., 111 SIAM J . Sci. Stat. Comput. 1984, 5 , 135. (4) Simon, Z.; Badileuscu, I.; Racovitan, T. J. Theor. Biol. 1977,66,485. Simon, Z . ; Dragomir, N.; Plauchithiu, M. G.; Holban, S . ; Glatt, H.; Kerek, F. Eur. J . Med. Chem. 1980, 15, 521. ( 5 ) Hopfinger, A. J. J . Am. Chem. SOC. 1980, 102, 7196. (6) Chose, A. K.; Crippen, G. M. J . Med. Chem. 1985, 28, 333 and references therein. (7) Cramer, R. D., 111; Milne, M. Abstracts of the ACS Meeting, April 1979, COMP 44. Wise, M.; Cramer, R. D.; Smith, D. M.; Exman, I. In Quantitative Approaches to Drug Design; Dearden, J. C., Ed.; Elsevier: Amsterdam, 1983; p 145. Wise, M. in Molecular Graphics and Drug Design; Burgen, A. S . V., Roberts, G. C. K., Tute, M. S., Elsevier: New York, 1986; pp 183-194. Cramer, R. D., 111; Bunce, J. D. In QSAR in Drug Design and Toxicology; Hadzi, D., Jerman-Blazic, B., Eds.; Elsevier: New York, 1987; P 3. (8) Goodford, P. J. J . Med. Chem. 1985, 28, 849. (9) Hansch, C.; Hathaway, B. A.; Guo, Z. R.; Selassie, C. D.; Dietrich, S . W.; Blaney, J. M.; Langridge, R.; Volz, K. W.; Kaufman, B. T. J . Med. Chem. 1984, 27, 129. (10) Dunn, J. F.; Nisula, B. C.; Rodbard, D. J . Clin. Endocrin. Metab. 1981, 63. ( I 1 ) Cramer, R. D., I11 Quant. Struct. Acf . Pharmacol., Chem. Biol. 1983, 2, 7, 13. Yunger, L. M.; Cramer, R. D., 111 Quant. Struc. Act. Relat. Pharmacol., Chem. Biol. 1983, 2, 149. (12) Westphal, U. Steroid-Protein Interactions I I ; Springer-Verlag: Berlin, 1986. ( 1 3) Vinter, J. G.; Davis, A.; Saunder, M. R. J . Comp-Aided Mol. Design 1987, 1, 31. (14) Gasteiger, J.; Marsili, M. Tetrahedron 1980, 36, 3219.

Virtual Compound Libraries: A New Approach to Decision Making in Molecular Discovery Research

Abstract The primary importance of molecular fields in biological recognition, attested by the number of reported successful CoMFA applications, suggests possible applications in 3D databases. In further support of this possibility, the probability of chance correlation using PLS in typical CoMFA applications is found to be about 5% or less for a cross-validated r 2 of 0.3 or greater, a “field fit” strategy for automating the alignment of molecules by seeking minimal differences in their fields is outlined, and a non-biological application of CoMFA, carbonyl hydration, is presented.

Designing Chemical Libraries for Lead Discovery

Virtual compound libraries, descriptions of all of the structures that might be produced by specified transformations involving specified reagents, are especially useful in molecular discovery when suitably fast and relevant searching techniques are available. Issues to be considered include fundamental data structures, neighborhood searching principles, useful searching approaches and techniques, library definition and construction, algorithmic details of library comparison, and user interfaces.

Solution Phase Synthesis of Chemical Libraries for Lead Discovery

The selection of compounds for use in high throughput biological assays is one of the critical factors that dictates the likelihood of detecting exploitable biological properties. In this paper, we present a process designed to deliver molecules that contain chemical functionality of immediate value in a lead discovery program, molecules that are sufficiently different from each other to ensure that redundancy of effort is avoided. The design process has already been implemented and used to add tens of thousands of reaction products to the Optiverse™ library

Crossvalidation, Bootstrapping, and Partial Least Squares Compared with Multiple Regression in Conventional QSAR Studies

By integrating advanced computational design and synthesis, a series of structurally diverse reaction products based on three core scaffolds were prepared by a propietary high throughput synthesis method. Incorporation of auto-mated work stations and sample handling techniques allowed for the production of more than 20,000 compounds in a relatively short time. A method to efficiently obtain quantitative and qualitative analytical data on these compounds was developed. Structural comparison of the individual members of this library with a database of clinical drug candidates revealed a significant degree of similarity based on Tanimoto coefficients.