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working to extend the range of combi­natorial chemistry, in which large col­lections, or libraries, of distinct molecu­lar entities are synthesized in parallel and then screened for potentially use­ful properties. His lab and others' labs have been involved in screening librar­ies consisting not only of antibodies and other biomolecules, but also poly­mers and small organic molecules. Schultz's goal, though, is to extend the combinatorial approach to the entire periodic table. This has led him and his coworkers to prepare and screen librar­ies of solid-state materials, such as high-temperature superconductors (C&EN, June 26, page 7).

In his lecture, a whirlwind survey of research from his lab, Schultz noted that high-temperature superconductors tend to have complex compositions and structures, and have been discov­ered largely by trial and error. The combinatorial approach is one way to carry out that discovery process more systematically and efficiently, he be­lieves. And to be sure, a "huge amount of chemistry" is waiting to be discov­ered in unexplored regions of the solid-state realm, he said.

Schultz ended up taking his mostly biologically minded listeners down (what was for them) a surprising and unfamiliar avenue. But his take-home message fit in beautifully with the "two cultures" theme of the sympo­sium: namely, that chemistry has a lot to learn from biology, and biology has a lot to learn from chemistry. •

New techniques enhance structure-activity work A group of industrial chemists and several academic collaborators are fo­menting revolution in the normally se­date field of quantitative structure-activity relationships (QSAR). They are developing a series of QSAR tech­niques they hope will replace some tra­ditional QSAR concepts that they be­lieve are over the hill.

In QSAR, mathematical "descriptors" of molecular structure are used as a ba­sis for predicting chemical and physical properties or biological activity. QSAR is widely used for drug discovery, among other applications. For example, it can be employed to predict partition coeffi­cients, lipophilicities, or relative reactivi-

38 OCTOBER 9,1995 C&EN

Ή : H I ur,

ties with biological receptors to support drug-design efforts. But Jack W. Frazer of Tuolumne, Calif., team leader of a sci­entific computing group that consults for Eastman Kodak, believes QSAR tech­niques up to now "have not realized their true potential//

Several years ago, Frazer's group was asked to bring new insights and computer technology to the drug dis­covery process at Kodak's Sterling Winthrop pharmaceuticals division (which has since been sold). Frazer says that when he evaluated QSAR as part of the drug-modeling process, "I decided some major changes would be required/'

For example, 'Traditional QSAR uses only one or two linear models to capture a ligand's noncovalent bonding interactions with a protein. . . . You can't capture such a complex thing with one or two simple linear models," says Frazer. And with traditional tech­niques, "QSAR practitioners never knew the quality of any prediction. When they made a prediction for an

unknown, they didn't know if it was good, bad, or indifferent."

So Frazer and coworkers began devel­oping a series of techniques that Frazer calls "a new paradigm" in QSAR. The techniques include a matrix of models to replace the traditional one or two linear QSAR models, new descriptors that re­late properties to local substructure pa­rameters, and equations to assess the quality of predictions. When Frazer pre­sented some of these concepts at a recent Gordon Research Conference on QSAR, he says, "A lot of people liked them but others said they plain didn't understand them.... I've come to the conclusion it's because the paradigm is so different from what they've been used to."

Frazer explains, "I classify a data set into subsets that are dominated by common noncovalent bonding interac­tions. Then, I construct a matrix of sev­eral hundred models using a special variable selection routine that puts me in the right part of descriptor space to make good predictions of the un­known's property values. This is much

different from what QSAR modelers are currently doing."

Frazer says his group also has devel­oped "a new cross-validation that moves you to the part of structure and descrip­tor space where the linearities more ac­curately point to the property value of interest. And we qualify every predic­tion. We haven't got the mathematics refined yet that will tell us the exact cor­rectness of predictions. But we can clas­sify predictions as being in three class­es—within the range of measurement noise, quite a ways beyond that range, or just indeterminate."

The matrix of models technique is also capable of modest extrapolation to prop­erties beyond the range of those in the original data set. For instance, Frazer and coworkers have used a data set on cholecystokinin inhibitors to demon­strate the ability of the technique to ac­curately predict new compounds with greatly improved property values. And Frazer believes new local surface de­scriptors the team is developing will fur­ther improve the technique's capabilities.

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Chemistry professor Curtis M. Bren-eman at Rensselaer Polytechnic Insti­tute, Troy, N.Y., who collaborates with Frazer's group, explains that tradition­al QSAR is generally good at predict­ing interpolated properties, but not ex­trapolated properties. "Extrapolation is something you don't do because the model blows up/ ' he says.

But Frazer's matrix of models tech­nique "is capable of extrapolation," he says, "and that's a really powerful thing. The way it does it is by not building just one of these models but by building sets of models and by analyzing how well these different sets predict properties."

The ability to extrapolate is impor­tant, says Breneman, because drug re­searchers often want to calculate prop­erties outside existing data sets. "They pretty much know what the activities or properties of molecules in their data set are," says Breneman, "but they want the home run, the drug that's go­ing to really be good. In order to do that you have to be able to extrapolate, to go outside your data set with your predictions."

In addition, he says, by using a ma­trix of models "you get more informa­tion than you do from a single model, so you have a better idea about the quality of the prediction you've made."

According to chemistry professor Pe­ter A. Politzer of the University of New Orleans, another academic collaborator of Frazer's, "A novel aspect of Frazer's approach is that it will permit the de­sign of new molecules with predicted and desired properties, rather than try­ing to predict the properties of already-designed molecules." Such an applica­tion can be viewed as inverse QSAR, says Politzer. "You decide what prop­erties are wanted, and then the method should give you molecules with those desired properties."

Another tool that enables modelers to propose sets of molecules with tai­lored properties is the transferable atom equivalent (TAE) modeling tech­nique, developed by Breneman and co­workers as part of the QSAR project. TAE-generated van der Waals surface properties of molecules can be used to produce high-quality QSAR indexes.

TAE modeling calculates electron densities and other properties of mole­cules of any size, including proteins. The technique assembles densities in atomic chunks, based on the "theory of atoms in molecules" developed by

Oxazole Tetrahydropyridine

Pyrrolidine Thiophene

Molecular surface ionization potential (encoded by different colors) is one of many parameters that can be estimated with the transferable atom equivalent (TAE) technique developed by Breneman and coworkers. Surface ionization potential affects the intermolecular interactions of molecules. Numerical data obtained from such TAE-generated plots can be used as descriptors in QSAR models.

chemistry professor Richard F. W. Ba-der of McMaster University, Hamilton, Ontario. "We've extended Bader's method to be able to actually recon­struct molecules from these atomic chunks, which we call transferable at­oms," says Breneman.

Breneman points out that TAE is philosophically similar to MEDLA (molecular electron density Lego as­sembler), a technique developed by Paul G. Mezey and a coworker at the departments of chemistry and of math­ematics and statistics of the University of Saskatchewan, Saskatoon (C&EN, Aug. 14, page 29). But unlike MEDLA, TAE can be used to obtain energetics data that can otherwise be calculated only by high-level quantum chemical calculations.

Mezey comments that he sees "the main advantage of Frazer's matrix of models method in the fact that it is multidimensional. Clearly, complex drug interactions cannot be modeled by one or two parameters. A logical

step is then to model molecules by a complete shape description, replacing quantitative structure-activity relation­ships, QSAR, with quantitative shape-activity relations, QShAR."

He adds, "In our laboratory we have developed shape group methods for QShAR studies in drug design and toxi-cological risk assessment, using quan­tum chemical shape descriptors of elec­tron density." Mezey has written a book on this topic—"Shape in Chemistry: An Introduction to Molecular Shape and Topology," VCH Publishers, New York, 1993.

According to Breneman, the rationale for supplementing older QSAR tech­niques with the matrix of models tech­nique and other new QSAR-related methods is, "Let's take what we have here and do a paradigm shift. Let's throw out a lot of old thoughts about things we can't do and try some new techniques that are more related to the actual structure of molecules."

Stu Borman

40 OCTOBER 9,1995 C&EN