Molecular Modeling in Organic Chemistry Correlating Odors with Molecular Structure Kenny B. Lipkowlz Indiana-Purdue University, Indianapolis, IN 46223 Computational chemistry is a new, multidisciplinary area of research that transcends traditional boundaries separating biology, chemistry, and physics. The importance of this area of research was recognized by the NSF when the chemistry division was realignid to accommodate computational research and when supercomputer centers were established nationwide. Its importance was also recognized by the Gordon Research Conference Board of Trustees, who recently avoroved the establishment of the Gordon Research Confere k e in Computational Chemistry, as well as by the American Chemical Societv. - . whose various subdivisions ~ r o m o t e this area of science. In addition to the lawe number of annual symposia in this field, the vitality of thi;new area of researchis most evident by the number of new journals dedicated LO computational chemistry. They include: Computers & Chemistry, Journal of Computational Chemistry, Theochem, Journal of Chemical and Informational Science, Journal of Chemometrics, Molecular Simulation, Journal of Molecular Graphics, and Journal of Comvuter-Aided Molecular Desien. - . all of which supplement existing journals that are replete with computational chemistw. Annual review series are beine established to keep track of:developments in the field, andemployment ~ r o s ~ e care t s eood for scientists well-versed in com~utation&methods. Unfortunately, the undergraduate chemistry curriculum of most schools is completel;devoid of comput&ional chemistry. Computational methods are not mentioned in textbooks, as they now are a t the graduate level, nor are they described in laboratory books. We helieve there is a need to reorganize the existing curriculum to reflect better science as it is now done. In this paper we present a safe, inexpensive, and successful undergraduate laboratory exercise that incorporates computational chemistry. We introduced the concept of structure-activity relationships (SAR) where the students prepare a series of related molecules to see how their odors change as the structure is modified. The change in odor is then correlated with molecular descriptors derived from modeling. Esters The molecules studied in this exercise are esters. Esters are selected because of their well-known "fruity" odors and because many (but not all) of them can be prepared easily and inexpensively via Fischer esterification ( I ) . Hence this exercise can be incorporated into a first-semester laboratory course if desired. The esters selected for study are acetates, CH~COZR, where the alcohol moiety of the ester, R, is to be changed. Each student or pair of students is assigned a different alcohol. The esters are prepared, purified, spectroscopically analyzed, and collected by the instructor so that the entire class has access to all esters for smelling. Not all esters are amenable to Fischer esterification. Either they cannot be made, or they are not easily extracted during normal workup. In any event, all esters considered here can be purchased from Aldrich Chemical Company so that no gaps exist in the series. The molecules considered in this report are
These molecules form a homologous series and include isomers that allow us to assess the influence of hranching. Molecular Modellng The computational component of this exercise involves molecular modeling with molecular mechanics. Molecular mechanics is a nonquantum mechanical method of obtaining structures, energies, and some properties of molecules. The method and its underlying philosophy has been reviewed by us in this Journal (2) and by others elsewhere (3). The theories involved are straightforward and are amenable to undergraduates in their sophomore year. A book on romputational chemistry that is easy to read and has a chapte;on this topic has recently been puhlished (4). The calculations are done with Model. an interactive graphics program that allows for rapid structure building. aeometrv optimization and molecular disolav . (.5.) . All moleiules ardeniered in an extended form only; that is to say, the aliphatic portion of the ester can adopt a variety of conformations, but only the linear, zigzag forms are considered for simplicity. Using Model the esters are constructed and then geometry optimized with the MM2 force field (6).Molecular dimensions are then ohtained along with dipole moments and other descriptors like accessible surface areas (7). An example of the output from this type of calculation is presented in the figure. The results are compiled in the table. An easy-toread review of molecular surface areas and volumes and their uses in structure-activity relationships has been published (8).Note that neither the dipole moments nor the polar surface areas change much from molecule to molecule. The
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Molecular Descriptors tor Esters 1-10
Molecule
Length A
1
5.4 6.7 7.9 6.1 9.2 7.9 7.9. 6.7 10.5 8.6
2 3 4 5 6 7 8 9 10
Width
Dipole (D)
Accessible Polar Surface Area (A2)
Accessible Nonpolar Surfate Area (A2)
Total Accessible Surface Area (A2)
1.95 1.95 1.95 1.99 1.95 1.96 1.95 2.00 1.95 1.95
31 30 30 27 30 25 32 29 30 30
75 99 119
106 129 149 145 169 162 174 193 189 185
3.1 3.1 3.1 4.4 3.1 3.9 4.4 4.4 3.1 4.4
--
NONPOLAR SATURATED SURFACE AREA NONPOLAR UNSATURATED SURFACE AREA POLAR SURFACE AREA
-
154.9 SO ANG ( K C 0 . 0 SO ANG (ACC30.4 50 ANG (ACC-
118 139 137 142 164 159 155
0.0)( 0.0)( 0.8)(
3 . 9 KCRL) 0 . 0 KCAL) -2.3 KCAL)
CDmputed accessible surface areas and stereoview of Me van der Waals surface of ester 10.
total surface areas do change hecause of the increase in nonpolar (aliphatic) surface areas. The nonpolar surface areamay be thought of as a measure of lipophilicity. It, along with the size and shape of the molecule, are the only things changing in the series.
Odors Caution should be exercised in smelling these molecules because some are listed in the Merck Index as having a narcotic effect in high concentrations. Consequently theesters are presented to students in 10-dram vials as a 3% solution propylene glycol (1,2-propanediol). Precisely how odors are perceived is not known. Indeed, categorizing these molecules is the most difficult aspect of the experiment. There are no hard numbers like LDso's (lethal half doses) or MIC's (minimum inhibitory concentrations) with which to correlate molecular descriptors. The categorization schemes are variable, changing from student to student. A "salty" smell descrihed by one student was descrihed as "Flintstone Chewables" by another, and even though students invariably find literature reports or books (5. 6) about odor classification, an amazing assortment of adjekives describing these simple esters isreported. Also, the relative odor intensities of these molecules are variable. Some, like methylacetate, were almost imperceptible while others were extraordinarily odoriferous. In snite of the suhiective interoretation of odors. a consensus of opinion is that the lower molecular weight esters, 14, smelled like nail polish or airplane glue, while the larger esters, 5-10, had the characteristic fruity odor associated with the ester functional s r o u ~The . "heaviness" or "fruitiness" of the latter esters &eases with size of alkyl group, and most students correlated fruitiness with nonpolar surface area. Provocative arguments were then made by stu-
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Journal of Chemical Education
dents about enhanced membrane transport of these molecules along with arguments about struckral fits in enzyme receptor sites. The students eventually had to predict what the odor of an unknown ester would he and then see how accurate their prediction was by preparing it. In our class we used the derivative of cvclohexanol. hut other alcohols would suffice. Summary
MYintention is to incorporate an element of computationa1 chemistry into an existing undergraduate curriculum. In this exercise students carry out molecular modeling to derive descriptors that may he used for correlating molecular shape with odor. We emplov molecular mechanics hecause it is an important compu&ional tool in organic chemistry and because the theory is at a level sophomore students can understand. The subjective nature of odor classification is an inherent weakness of the exercise. No straight-line plots nor concise summaries will appear in the ensuing laboratory reports. This represents areal-world problem, and students see early in their careers that answers are not always black or white. They see, first-hand,. the . problems that researchers in flavor and fra&ance companies or pharmaceutical companies have in devising s t r u c t u r ~ c t i v i t yrelationships. The suhjective natureof this laboratory exercise is also the sourceof immeasurable fun and endless questions for inquisitive minds. The exercise outlined above is flexible: other common functional groups with their own distincti"e smells can he computed with molecular mechanics and can be used instead of esters. This type of exercise can also be incorporated into an oreanic chemistw course rather than the laboratorv. The ester\olutions can be left next to a graphics termind, and modeling can he done without synthesis. Either way this
is an enjoyable exercise to teach and one which introduces computational chemistry into the undergraduate curricu. lum. Literature Cited Bamn: Bmton, 1986. 2. Boyd, D. B.; Lipkawitz, K.9.J. Cham. Educ. 1982.59.269-274. 3. Burkert, U.:Allinger, N. L. Molecvlor Mechanics: ACS Monoeraph 177: Arneriean
5. ModelrvnaonanyVU(com~uterandrequirpaaTektmnix4107or4207tennindwith bifpad. The software k available from the author without eosr Other veraions of Model exist that require only en 1BM PC. Different modeling aaftware can dm be used. Cantact theauthor far further information, tel. 317-274-6883. 6. Allinger, N, L. J.Am. Chom.Soc. 1977.99.8127. 7. TheLecRichadsdgorithm iausedlor this. Lee,B.:Riehards.F'. M. J.Mol.Biol.1971, 55, 379. 8. ~earlrnan,R. S. ~ h y r i e o lchemieol Roperties o f ~ r u g s~slkowksy, ; H..sinkula. A,, valvani, S., Ed% Medicinal Research Series, Vol 10: Marcel Dekker: New York,
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