516
Anal. Chem. 1982, 54, 516-521
Table IX. Compositions of Cis-Trans Mixtures Determined with and without Gated Decoupling
premixed sample BDOA 1 2 3
composition (% cis) measda gated decoupling known with without 74.30 45.81 18.27
75.04 43.88 19.12
73.95 44.70 18.93
37.09 60.34 40.41
36.73 60.16 39.97
38.25 59.86 42.01
29.48 53.52 25.63
30.31 54.09 25.05
30.40 55.19 24.80
NDOA 1 2 3
DOA 1 2 3
a The measured values are average values of dual determinations based on 3'-methyl- and 4'-carbon resonance integrals. Coefficient of variation: with gated decoupling, 2-4%; without gated decoupling, 2-6%.
decoupling technique eliminates the residual NOE during data acquisition for integrals has been substantiated by the spectral analysis of a homogeneous sample containing a pure isomer. On the basis of the uniformity of NOE's received by the structurally similar cis-trans carbons, one would expect no appreciable effects of the different modes of decoupling (with vs. without gated) on the relative peak intensities of cis-trans carbon resonances. The data in Table IX illustrate that the two decoupling methods, with and without gated decoupling modes, can be used in the quantitative analysis of the com-
position of cis-trans mixtures of terpene aminoethanols. Both decoupling methods yielded comparable results (Table IX). The generality of the 13CNMR spectral behavior with the three types of unsaturated terpene aminoethanols indicates that the technique developed in the present study can be applied to the quantitative characterization of isomeric octadienyl groups in other closely similar compounds. This method provides a means for studying stereospecific interactions of the fish toxicant BDOA and the nitrosamine NDOA with biological systems.
ACKNOWLEDGMENT The author thanks Glen Hill and Steve Calister for technical assistance. LITERATURE CITED (1) Abidi, S. L., paper presented at 180th National Meeting of the American Chemical Society, Las Vegas, NV, Aug 25-29, 1980; AGFD 11. (2) Abidi. S. L., paper presented at 180th National Meeting of the American Chemical Society, Las Vegas, NV, Aug 25-29, 1980: ANAL 141. (3) Abidi, S. L.; Idelson, A. L. J . LabelledCompd. Radiopharm. 1981, 18, 1215-1225. (4) Void, R. L.; Waugh, J. S.; Klein, M. P.; Phelps, D. E. J . Chem. Phys. 1988, 48, 3831. (5) Abidi, S. L.; Finch, R. A. J . Environ. Mutagenesis 1981, 3 , 587-591. (6) Johnson, L. F.; Jankowskl, W. C. "Carbon-13 NMR Spectra"; WlieyIntersclence: New York, 1972; pp 41, 119. (7) Abldl, S. L.; Idelson, A. L. Proceedings of 4th International Symposlum on Stable Isotopes, Jeulich, West Germany, March 23-27, 1981, I n "Analytical Chemistry Symposium Series", Elsevier: Amsterdam, The Netherlands, in press. (8) Abidi, S. L., paper presented at Great LakedCentral Regional Meeting of the American Chemical Society, Dayton, OH, May 20-22, 1981; ORGN 282. (9) Abldl, S. L., unpublished results from National Fishery Research Laboratory, La Crosse, WI, 1979.
RECEIVEDfor review September 21,1981. Accepted December 3, 1981.
Computer Prediction of Substructures from Carbon- 13 Nuclear Magnetic Resonance Spectra Cralg A. Shelley" and Morton
E. Munk'
Research Laboratories, Eastman Kodak Company, Rochester, New York 14650
A program that searches a data base of asslgned "C NMR spectra to find reference structures that model an unknown is described. The program exhaustlveiy searches the data base for substructure-subspectrum pairs that are consistent with the spectrum of the unknown. A test to evaluate the interpretive character of library search programs is suggested. The test criterion is met by the Interpretive procedure described. Retrieved reference compounds are organized on the basis of the substructure predictions. The spectroscopist-user interactively displays the structural diagrams of the retrieved reference structures.
New applications of computer technology are continuing to influence the practice of chemistry. The computer is increasingly applied as a tool to assist the chemist in elucidation and verification of chemical structures. I3C NMR spectromDepartment of Chemistry, Arizona State University, Tempe, AZ 85281. 0003-2700/82/0354-0516$01.25/0
etry is one area receiving increasing attention because of the well-defined nature of chemical shifts, multiplicities, and spectrum-structure correlations (assignments). In developing computer tools in 13C NMR, three main avenues have been pursued-pattern recognition, artificial intelligence, and the search of spectroscopic libraries. The boundaries between these categories are sometimes not distinct. Pattern recognition has been used to interpret localized structural features such as functional groups ( 1 ) and to interpret global features such as chemical class and skeletal features (2). Artificial intelligence applications may be considered to be those methods that use a "knowledge base" of spectrastructure correlations that is at least more abstract than a reference library. (Because the boundary between artificial intelligence and other techniques is often hazy, we have chosen to define it to reflect the various reported applications of the method to computer-assisted structure elucidation.) Carhart and Djerassi have developed a program to elucidate the structure of acyclic monofunctional amines (3). Mitchell and Schwenzer described spectrum-structure correlations derived from a data base using the Meta-DENDRAL approach ( 4 ) . 0 1982 American Chemical Society
ANALYTICAL CHEMISTRY, VOL. 54, NO. 3, MARCH 1982
Programs have also appeared that predict (or simulate) chemical lshifts (5, 6) and the number of signals in broadband-decoupledspectra (7). Empirical correlations have been an integral part of the CHEMICS structure elucidation system for many years (8). Applications of library search in 13CNMR are characterized best by the approaches of Bremser and co-workers (9,IO) and Clerc and co-workers ( 2 1 ) . All approaches to library search in 13C NMR have been designed with computer-assisted interpretation of the spectrum in mind. An interpretive library search is one that attempts to find reference structures whose spectra contain “subspectra” ( 9 ) consistent with the spectrum of the unknown. A subspectrum corresponds to a substructure of the reference that potentially models a substructure of the unknown. This definition of interpretive search implies that those parts of the reference structure that do not model the unknown do not hinder the interpretive process, despite the lack of similarity in the physical data due to the remainder of the molecules. The Bremser search strategy (9) makes use of a reference file that includes for each entry the structure, chemical shift assignments, signal multiplicity, and characteristic substructures selected by the spectroscopist. These explicitly defined substructures are normally ring systems or a sequence of three to six carbon atoms joined to one another. For each reference entry the program determines the number of corresponding signals. Corresponding signals have a chemical shift within a given tolerance, typically 1-2 ppm, and consistent multiplicity. Two threshold criteria must be met to retrieve a reference spectrum. First, a minimum number of signals in the reference spectrum must have corresponding lines in the spectrum of the unknown. Second, all signals except one in a reference subspectrum of an explicitly defined substructure must be matched by the spectrum of the unknown. If these conditions are met, a similarity number is calculated that is a function of the number of corresponding signals, the number of signals in the spectrum of the reference, and the number of signals in the spectrum of the unknown. Bremser has implemented a new library search that produces results similar to those of the previous search but “greatly reduces computing time” (IO). The program uses the chemical shift and “reduced”multiplicity: even (doublet and quartet) and odd (singlet and triplet). The similarity is a function of the probability of a signal match or mismatch. The probability of a chemical shift match at m ppm is the sum of the chemical shifts at m ppm in the data base divided by the average number of signals per ppm in the data base. The characteristic substructures selected by the spectroscopistand included in the data base (9) are searched separately as “subreferences”by the same procedure. These substructures are no longer used to screen reference spectra for matches. The Clerc similarity function (11)is based on a bit map (“signature”) representing features derived from the spectroscopic data. The spectroscopist selects the spectral features represented by the bit map. An example of a rule defining a feature is “if the number of lines in a given shift range is larger than a threshold, then set the bit correspondingto this feature to 1.; otherwise set it to 0”. The bit map representation of spectral data has important advantages in designing searches based on combined spectroscopic sources, e.g., NMR, IR, and mass spectra. The bit maps for the unknown and the reference are compared bit by bit to calculate the similarity. Four results are possible for each bit comparison: (1)both bits are l’s, (2) the bit for the unknown is 1 and the bit for the reference is 0,(3) the bit for the unknown is 0 and the bit for the reference is 1,or (4)both bits are 0’s. Each of the four conditions is assigned a “weight”for each feature. The
Unknown
Reference 1
517
Reference 2
Flgure 1. A simplified search problem. INTERPRETIVE
SOME OR NO INTERPRETIVE CAPABILITY
n
Flgure 2. Test to determine interpretlve capability.
similarity function returns the sum of the weights for all features. It is apparent that the function can be efficiently executed. The bit map representation for the spectral data is also extremely compact. Feature selection by the spectroscopist provides a )mechanismto incorporatehis “heuristic” knowledge into the similarity measure; however, the feature selection process may also be biased and the selection ta:ik difficult. Pattern recognition may be a useful tool to assist the spectroscopist in selecting features and assigning weights.
GOAL OF THE STUDY An interpretive library search should be insensitive to the presence of nonoverlapping signals in the spectrum of either the unknown or the reference. This performance characteristic is illustrated in Figure 1. The reference file contains two entries that model substructures of the unknown. Reference 1 models substructure B, but its spectrum includes a substantial number of signals for structure component A that do not overlap the spectrum of the unknown. Reference 2 models substructure C, but thelsignals for substructure D do not overlap the spectrum of the unknown. In an interpretive library search, the identification of substructure B in reference 1 and substructure (2 in reference 2 is not affected by the presence of the nonoverlapping spectral data due to substructures A and C and to D and B, respectively. The following test should indicate the sensitivity of a search to nonoverlapping spectral data. A search based on thie spectrum of an unknown is executed, and the set of retrieved reference structures is saved. Some of the lines in the l31C NMR spectrum of the unknown are deleted to form a partiid spectrum of the unknown. Another search is run, and the set of retrieved reference structures is compared with the set obtained for the previous complete spectrum search. An interpretive search requires that the set for the partial spectrum search is a subset of the set obtained for the cornplete spectrum search (the two sets may be identical). No intersection between the two sets indicates that the search is not interpretive. (Certainly the partial spectrum selected may affect the search results; however, an entirely interpretive search should still meet the subset criterion.) This concept is graphically depicted in Figure 2. The large circle represents the set of reference structures retrieved for the complete spectrum search. The small circle represents the set of reference structures retrieved for the partial spectrum search (circle size does not imply set size). Search programs that do not meet the subset criterion may have limited predictive capability. (The intersection set size is dependent on both the search procedure and the reference library for searches with limited interpretive capability.) Again consider the example in Figure 1. A search with poor interpretive capability does not retrieve reference 1 because of the nonoverlapping spectral data for A and C; however, the bias toward C in the unknown and the decreased amount of
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ANALYTICAL CHEMISTRY, VOL. 54, NO. 3, MARCH 1982
Table I. Literature References for the Data Base no. of no. of spectra/ref spectra refs 1-5 206 102 6-10 95 12 11-30 175 12 31-191 509 5 spectral data for D lead to the retrieval of reference 2. In a second search, a few lines are deleted from the spectrum of the unknown that correspond to carbon atoms in component C. Now the similarity function is biased toward component B. Reference 1is retrieved and reference 2 is missed. There is no intersection between the two sets of retrieved reference structures. The test criterion has indicated that the search is not interpretive. An interpretive search would behave quite differently with the data of Figure 1. A search of the fie would retrieve both reference 1 and reference 2. The search is rerun after some signals corresponding to C are deleted. This does not affect the retrieval of reference 1 and may affect the retrieval of component C (depending on the lines deleted). The subset criterion of the interpretive test discussed above is met. The program goal was to design an interpretive search that would meet the test described. Assigned 13C NMR spectra represent the substructure-subspectrum correlations that are required to achieve this goal. We recognize, however, that there are other characteristics of an effective library search program that must be incorporated, e.g., low sensitivity to variations in experimental conditions.
DATA BASE A data base of completely assigned 13CNMR spectra was created in order to develop and implement the interpretive library search procedure. The file consists of the connection tables of 985 structures with a chemical shift assigned to each carbon atom. Spectra were extracted from 131 literature references and generally selected on the basis of relevance to natural-products chemistry. The number of spectra extracted from a reference vs. the number of references is shown in Table I. The five most fruitful references were as follows: Breitmaier and Voelter (12),191 spectra; Blunt and Stothers (13), 136 spectra; Jankowski and Johnson (14),97 spectra; Hansen (15),54 spectra; Wenkert et al. (16),31 spectra. The chemical shifts were rounded to the nearest 0.1 ppm increment from tetramethylsilane. The program used to enter the assigned spectra to the data base was a slight modification of the linear code input procedure used in the CASE program (17). In addition to the connection table and assigned chemical shifts, only the literature reference was included. The identity of the connection tables was checked by displaying the structural diagram with a computer program and making a visual comparison. All library search methods are dependent on the quality of the data and the heterogeneity of the structures included in the reference library. The data base described here was created to develop and test the interpretive library search method. To serve as a practical tool of structure elucidation, the file requires expansion with particular attention to increasing structural deversity. METHOD On comparison of chemical shifts of the unknown with each reference spectrum, a tolerance is used that is a function of the chemical shift. This function is simply a table of tolerance vs. chemical shift created by the following procedure. First, a histogram of the entire 13C NMR chemical shift range is obtained for each of the seven possible multiplicity assignments: quartet, triplet, doublet, singlet, even, odd, and un-
defined. Each histogram depicts the number of occurrences of a particular chemical shift of given multiplicity assignment VS. the chemical shift at 0.1 ppm increments. Next, for each histogram the average number of occurrences per 0.1 ppm increment is calculated. The average number of occurrences per 0.1 ppm increment is now used to determine the average number of occurrences over a fm ppm range, x . (The value of m currently used is 1.5 ppm; the optimal value has not been determined.) Finally, for each histogram the tolerance, f t ppm, at each 0.1 ppm increment is set to the range in ppm required to include exactly x occurrences. The tolerance assigned by the function decreases as the frequency of signal occurrence increases and increases in less populated regions. This variable window diminishes the influence of chemical shift on the number of matched signals. A minimum and a maximum tolerance overrides the calculation of unacceptably small or large tolerances. Presently, a minimum tolerance of 1.0 ppm and a maximum tolerance of 10.0 ppm are used. The program (Figure 3) searches the data base for substructure-subspectrum pairs that correspond to the experimental data. The procedure resembles a substructure search that uses spectral data and structure to guide it. A comparison begins with each carbon atom of a reference structure being flagged if its assigned shift and multiplicity correspond to a signal in the spectrum of the unknown. Note that it is possible for a single signal of the reference spectrum to be assigned to more than one carbon atom. Next, the number of flagged carbon atoms is compared with a threshold value n (currently n = 4). If this threshold is met, the connection table for the reference structure is exhaustively searched for all substructures that contain only flagged carbon atoms and heteroatoms. These substructures are screened by deleting those for which the corresponding subspectrum contains fewer than n distinct signals. Substructuresubspectrum pairs that meet these tests are considered to be possible interpretations of the experimental data. Reference structures containing one or more substructures are retrieved by the search. The test criterion to assess the interpretive character of a library search procedure is intrinsically satisfied by this procedure. A search based on a partial 13CNMR spectrum, formed by arbitrarily deleting signals, may result in fewer carbon atoms being flagged in a reference structure. Consequently, the number of substructures containing only flagged carbon atoms and heteroatoms meeting the threshold criteria in a reference structure must either decrease or remain the same. Therefore, the set of reference structures retrieved for a partial spectrum search must be the same or a subset of the set obtained for the complete spectrum search. Thus, the program is not biased by nonoverlapping spectral data in retrieving structures. The search program organizes the retrieved reference structures on the basis of the predicted substructures. A substructure contains the connectivity of matching carbon atoms and heteroatoms, the charge of each atom, and the types of bonds to nonhydrogen atoms for each atom. Each predicted substructure is assigned a canonical name. The coding program used for naming substructures is identical with that in the CASE program (18). By use of the canonical name of each substructure, a complete and nonredundant list of the substructures is created. For each unique substructure, a linked list of corresponding reference structure numbers is also created. This data organization facilitates the examination of the search results, the predicted substructures, by the chemist.
USER INTERFACE The program is controlled by an interactive commanddriven interface. The command to initiate a new search
ANALYTICAL CHEMISTRY, VOL. 54, NO. 3, MARCH 1982
prompts the user for chemical shifts and the corresponding multiplicity assignments: quartet, triplet, doublet, singlet, even, odd, and unknown multiplicity. Other commands allow the user to peruse the list of interpreted substructures, each of which is output embedded in the parent structure. The parent structure is displayed directly from the connection table on a graphics terminal BS a conventional structural diagram (19). Each carbon atom in the substructure is marked by an asterisk. Heteroatoms attached to marked carbon atoms are also part of the substructure. The substructure predictions are organized by using the number of signals in the corresponding subspectrum and the sum of the chemical shift deviations of the subupectrum signals from the nearest matching signals in the spectrum of the unknown. The substructure with the most subspectrum signals and the smallest chemical shift deviation is at the top of the list. The substructure with the fewest subspectrum signals and the largest chemical shift deviation is at the bottom of the list. For each substructure prediction, the user may elect to display any or all reference structures containing the predicted substructure. Other commands allow the user to get hard copy output of' the predicted substructures. The user may also interactively repeat a search with a tolerance increase or decrease using commands which divide or multiply the previous tolerance by 2. This feature is useful when the number of retrieved reference structures is too small or large.
EXAMPLES Although any library search is strongly dependent on the heterogeneity and applicability of the reference library, insight may be gained by reviewing actual search results. In the following examples, substructure predictions that contain fewer than six signals in the corresponding subspectrum that are redundant or contained within larger substructure predictions have been excluded in most instances to conserve space; however, predictions that are very informative but do not meet these criteria have been included, as noted in the paper. A search using the spectral data of structure 1, including multiplicities, lled to the substructure predictions shown in structures 2-4. Structures 2-4, were output by the program on a raster graphics terminal. The predicted substructures include the carbon atoms flagged with an asterisk and the attached heteroatoms. The reference file contains structure 1 and a number of structures similar to it. The many structural analogues to 1 in the file enabled the program to detect large substructures whose subspectra are consistent with the data of 1. However, other library search procedures may also retrieve these reference spectra because there are few nonoverlapping spectral data between 1 and 2-4. In other words, none of these predictions readily indicates the ability of this procedure to detect relevant substructures when many nonoverlapping spectral data exist. The complete search output also contained a number of substructure predictions contained in references with few overlapping spectral data, e.g., structure 5. The next three examples were used by Bremser and coworkers to illustrate the performance of their search system (9, 10). None of these samples are included in the reference file. Although Bremser did not include the complete spectral data in his papers, the spectral data did exist in his microfiche catalog (20). Entering the spectral data for structure 6 (426 in 20), including multiplicities, led to the substructure prediction in 7. This substructure appears to result from a large number of aromatic carbons that happen to have matching shifts. The numbers in structures 8 and 9 cross-reference corresponding chemical shifts. These shift correspondences substantiate that this substructure prediction is poor. A number of aromatic substructure medictions with fewer than six chemical shifls in the correspodding subspectrum were also
I
519
II
I
ti
5
2
N
N
7
retrieved. Although the predictions for 6 are limited, the library has no st,ructuwallysimilar references. This is indicatied by retrieving references that contained atom-centered substructures that are also in 6. Only the unsubstituted aromatic carbon atoms had matching atom-centeredsubstructures with a bond radius greater than 1 bond.
R
!z
9
?!
N
N
287,
10
N
*\*9*\*/*..,+*\*/* I
I
T
II
N
O
H
5!
The chemical shifts without multiplicity for 10 (372 in 20) led to predictions contained in structures 11-15. Other predictions, e.g., 16, are contained in large molecules with substantial nonoverlapping spectral data. structures 13-15 are
520
ANALYTICAL CHEMISTRY, VOL. 54, NO. 3,MARCH 1982
not retrieved when the multiplicities are entered. When multiplicity information is entered, all substructure predictions (including those with less than six signals in the subspectrum) are structurally consistent with the unknown.
.\;"
Signal Multiplicity
k\*/*
I
Input Reference
;"I
.A!,*,
Compare Signal-by-Signal and Flag Carbon Atoms in Relerence Structure that
HO
on I
?!
16
N
At Least n
Yes
17 N
.-* +,* '
I
Search Reference Structure CT for Substructures Containing Only Flagged Carbon Atoms and Heteroatoms
4 .
Delete Substructures Whose Subspectra Contain Fewer than n Signals
i
Canonically Name Each Substructure
A Duplicate?
The chemical shifts and multiplicity for 17 (1411 in 20) led to predictions contained in structures 18-28. Many of the predictions not shown contained substructure predictions that were part of larger substructure predictions. The reference file contained one benzoate, 29, which was retrieved but had fewer than six signals in the corresponding subspectrum.
to Substructure
Figure 3. Program flow chart.
DISCUSSION The interpretive library search program described in this paper has several advantages over other approaches. First, the procedure is not biased by nonoverlapping spectral data in comparing a spectrum of a reference entry to the spectrum of the unknown. Second, a subspectrum match is the only requirement for substructure prediction. There are no limitations imposed on the nature or the size of the substructures detected other than the minimum number of signals in the corresponding subspectrum. Substructures do not need to be predefined by a spectroscopist. Instead, the connection table is used dynamically in the search strategy. Finally, the graphical output of retrieved reference structures with embedded substructures simplifies the review of the search output by the spectroscopist. The "knowledge base" of the program is a collection of assigned I3C NMR spectra. Unassigned spectra in the file serve no purpose in a subspectrum matching strategy; however, partially assigned spectra can be used for the carbon atoms which have been assigned chemical shifts. At the present stage of reference library development,the lack of broad structural diversity is a severe limitation to wide application of the program. This deficiency can lead to few, if any, substructure predictions for a given subspectrum of the unknown. Of course, the performance of all library search systems is limited by the extent of structural heterogeneity,the relevance of the reference structures, and the quality of the spectral data. It
ANALYTICAL CHEMISTRY, VOL. 54, NO. 3, MARCH 1982
Reference Entry
Unknown Structure
Figure 4. Influence of boundaries on predicted substructures.
is important to note that the reliability of predictions by the program is not substantially affected by the quality of spectral data. Two situations can be envisaged. Incorrect assignment could result either in the failure of the program to flag one or more carbon atoms in a reference structure or in the flagging of one or more carbon atoms incorrectly. The former could lead to a missed substructure prediction or one of decreased size. The chance of incorrectly predicting a substructure as a result of the latter is small because neighboring atoms in the connection table must also be flagged. Thus, the number and size of retrieved substructures are affected far more than the reliability of substructure predictions. The intrinsic insensitivity of the program to the nonoverlapping spectral data is one of its strongest assets. The search strategy that achieves that program characteristic does, however, influence the boundaries of the substructures predicted. In general, the predicted substructure common to both unknown and reference entry is smaller than the actual substructure in common. This relationship between search strategy and the size of the predicted substructure is graphically illustrated in Figure 4. The substructure common to the reference entry and the unknown is (A + B), but the program only identifies substructure A which is contained in (A + B). This occurs because the chemical shifts of B-layer carbon atoms of the common substructure are, as expected, more profoundly influenced by the structurally differing environments C and D than are the chemical shifts of A-layer carbon atoms. Thus, whereas A-layer carbon atoms of reference entry and unknown match in chemical shift, B-layer carbon atoms may not. The degree of match in B-layer atoms, i.e., the extent to which the actual common substructure boundary is approached, depends on the degree of environmental difference between C and D. Even though the program may not identify the outermost boundary of the common substructure, the user is assisted in doing so by the nature of the output, i.e., embedding substructure predictions in the parent reference structure. As the limitations to wide program application presently imposed by the reference library are reduced by expanding its structural heterogeneity, new problems will have to be addressed. If the unknown structure is modeled by a large
521
number of reference structures, voluminous predictions will be obtained. The review of such output, although facilitated by the ability of the user to limit, even to one, the number of reference structures examined for each predicted substructure, may prove burdensome because of a large number of structurally similar substructures. To deal with this matter, a routine to identify substructures entirely contained in larger substructures will be added. In addition, to extend the interpretive character of the program, computer perception of structural commonality (overlap) between predicted substructures will be included. Although the efficiency of the search method has not been analyzed, the procedure gives reasonable real-time response. The program is currently implemented in an interactive environment on a large mainframe (an IBM 3033 computer using TSO).
LITERATURE CITED Wilklns, C. L.; Isenhour, T. L. Anal. Chem. 1075, 47, 1849-1651. Woodruff, H. B.; Sneliing, C. R.; Shelley, C. A.; Munk. M. E. Anal. Chem. 1077, 49, 2075-2080. Carhart. R. E.; Djerassi, C. J. Chem. Soc.,Perkin Trans. 2 1073,
1753-1759. Mitchell. T. M.; Schwenzer, G. M. Org. Magn. Reson. 1078, 1 1 ,
378-364.
Clerc, J. T.; Sommerauer, H. Anal. Chlm. Acta 1077, 9 5 , 33-40. Smith,D. H.; Jws, P. C. J. Am. Chem. Soc. 1078, 100, 3316-3321. Shelley, C. A.; Munk, M. E. Anal. Chem. 1078, 50, 1522-1527. Yemasaki, T.; Abe, H.; Kudo, Y.; Sasaki, S.ACS Symp. Ser. 1077, NO. 5 4 , 108-125. Bremsef. W.: Kller. M.; Meyer, E. Urg. Magn. Reson. 1075, 7 ,
97-106. Bremser, W. Z . Anal. Chem. 1077, 286, 1-13. Schwarzenbach, R.; Meill. J.; Konitrer, H.; Cierc, J. T. Org. Magn. Reson. 1076, 8 . 11-16. Breitmaier, E.; Voeker, W: “ G I 3 NMR Spectroscopy, Methods and Application”, 1st ed.; VerlagChemle: Weinhelm, 1974; Vol. 5; Moncgr. Mod. Chem. Blunt, J. W.; Stothers, J. B. Org. Magn. Reson. 1077, 9 , 439-464. Jankowskl, W. C.; Johnson, L. F. “Carbon-13 NMR Spectra, A Coilectlon of Assigned, Coded and Icdexed Spectra”, 1st ed.;Interscience: New York, 1972. Hansen, P. E. Org. Magn. Reson. 1070, 12, 109-142. Wenkert, E.; Chang, C.; Chawla, H. P. S.;Cochran, D. W.; Hagaman, E. W.; Kina. J. C.: Orito. K. J. Am. Chem. Soc. 1076. 98.
3645-3655.Shelley, C. A.; Woodruff, H. B.; Sneiiing, C. R.; Munk, M. E. A&S Symp. S e r . 1077, No. 54, 92-107. Shelley, C. A.; Munk, M. E. J. Chem. Inf. Comput. Scl. 1070, 19, 247-250. - . . -. ..
Shelley, C. A. National Meeting of the American Chemical Society, Computers In Chemistry Dtvision, New York, Aug 23-28, 1981. Bremser. W.; Emst, L.; Franke, B.; Qerhards, R.; Hardt, A. “Carbon-I3 NMR Spectral Data”; VerlagChemle: Weinhelm, 1979.
RECEIVED for review May 11,1981. Accepted December 14, 1981. Partial financial support by the National Institute of General Medical Sciences (GM21703) is gratefully acknowledged.
