Jorgensen reports an enhanced QM/MM and ab initio study of the rate enhancement of Diels-Alder reactions in various solvents.¹ This study extends earlier studies that he and others have done, many of which are discussed in Chapter 6.2 of the book. In this study, he reports QM/MM computations using the PDDG/PM3 method for the QM component, and MP2 computations incorporating CPCM to account for bulk solvent effects.

The major advance in methodology in this paper is performing a two-dimensional potential of mean force analysis where these two dimensions correspond to the forming C-C distances. In addition, computations were done for water, methanol, acetonitrile and hexane as solvents. Highlights of the results are listed in Table 1.

Table 1. Computed bond asynchronicity^a and activation energy^b (kcal/mol) for the Diels-Alder reaction with cyclopentadiene.

The semi-empirical method underestimates the asynchronicity of these gas-phase Diels-Alder TSs. However, with inclusion of the solvent, the computations do indicate a growing asynchronicity with solvent polarity, This is associated with the ability of the solvent, especially protic solvents, to preferentially hydrogen bond to the carbonyl in the TS.

In terms of energetics, in must first be pointed out that the computations dramatically overestimate the activation barriers. However, the relative trends are reproduced: the barrier increases from water to methanol to acetonitrile to hexane. Jorgensen also computed the activation barriers at MP2/6-311+G(2d,p) with CPCM using the CBS=QB3 gas phase geometries. Some of these results are listed in Table 2. The results for water are in outstanding agreement with experiment. However, the results for the other solvents are poor, underestimating the increase in barrier in moving to the more polar solvent.

Table 2. MP2/6-311+G(2d,p)/CPCM values for ΔG^‡ (kcal/mol).

Bottom line, the conclusions of this study are in agreement with the earlier studies, namely that the hydrophobic effect (better may be the enforced hydrophobic interaction) and greater hydrogen bonding in the TS (both more and stronger hydrogen bonds) account for the rate acceleration of the Diels-Alder reaction in water.

References

(1) Acevedo, O.; Jorgensen, W. L., "Understanding Rate Accelerations for Diels-Alder Reactions in Solution Using Enhanced QM/MM Methodology," J. Chem. Theory Comput. 2007, 3, 1412-1419, DOI: 10.1021/ct700078b.

A couple of additional papers have pointed out systematic problems with using DFT and offer guidelines for methods that provide accurate results. These complement my previous posts on the subject Problems with DFT and Problems with DFT – an Update.

Grimme¹ takes the approach of benchmarking methods and basis sets using isomerization energies, examples of which are shown in Scheme 1. Computed isomerization reaction energies are compared against experimental values or, in a few cases, against extrapolated CCSD(T) energies using cc-pVXZ (X=D-T or X=T-Q). This extrapolation technique² is a way to estimate the complete basis set energy.

Scheme 1.

In terms of basis set, the error systematically decreases with increasing size of the basis set when the SCS-MP2 method is used to compute the energies. Surprisingly, the error is essentially constant for all the basis sets with B3LYP. The root-mean-square deviation and maximum error for the isomerization energies computed with the TZV(2df,2pd) basis set and a variety of different methods are listed in Table 1. Both CCSD(T) and SCS-MP2 provide truly excellent results. Since the later method is much more computationally efficient that the former, Grimme argues that this is really the method of choice for accurate energies. DFT methods vary in their performance, with no discernable trend based on what type of DFT it is (i.e. meta-GGA, hybrid GGA, or hybrid meta-GGA). Of no surprise, based on lots of recent studies (including those blogged about in ), the performance of B3LYP is likely to be problematic.

Table 1. Errors in Computed Isomerization Energies (kcal/mol)

In a related study, Bond³ explores the ability of the composite methods to predict enthalpies and free energies of formation for a set of nearly 300 compounds. Bond makes use of isodesmic and homodesmotic reactions (discussed in Chapter 2). His results for the mean absolute deviations of ΔH are given in Table 2. All of the composite methods (see Chapter 1.2.6) provide quite acceptable results. Once again, B3LYP is shown to be incapable of predicting accurate energies.

Table 2. Mean average deviation in predicted heats of formation compared
to literature values.

References

(1) Grimme, S.; Steinmetz, M.; Korth, M., "How to Compute Isomerization Energies of Organic Molecules with Quantum Chemical Methods," J. Org. Chem., 2007, 72, 2118-2126, DOI: 10.1021/jo062446p.

(2) Helgaker, H.; Klopper, W.; Koch, H.; Noga, J., "Basis-Set Convergence of Correlated Calculations on Water," J. Chem. Phys., 1997, 106, 9639-9646, DOI: 10.1063/1.473863

(3) Bond, D., "Computational Methods in Organic Thermochemistry. 1. Hydrocarbon Enthalpies and Free Energies of Formation," J. Org. Chem. 2007, 72, 5555-5566, DOI: 10.1021/jo070383k

The old EMSL Gaussian Basis Set Order Form (http://www.emsl.pnl.gov/forms/basisform.html) has now been updated to include a very nice interface. The new service is called Basis Set Exchange and is available at https://bse.pnl.gov/bse/portal.

This new service is built off of web 2.0 tools. Most critically, the basis sets are now stored in an XML format that builds upon Chemical Markup Language (CML). Not only can users get a wide variety of basis sets for most elements, basis set developers can upload their basis sets for curation and delivery. The design and implementation of this service is described in a recent article.¹

References

(1) Schuchardt, K. L.; Didier, B. T.; Elsethagen, T.; Sun, L.; Gurumoorthi, V.; Chase, J.; Li, J.; Windus, T. L., “Basis Set Exchange: A Community Database for Computational Sciences,” J. Chem. Inf. Model 2007, 47, 1045-1052, DOI: 10.1021/ci600510j.

In section 4.4 of the book, I discuss in great detail the computational (and some experimental) studies of the benzynes, the formal diradicals created by loss of two hydrogen atoms from benzene. Now comes a very nice experimental study on a molecule that takes the next step: 1,3,5-tridehydrobenzene 1, benzene that lacks three hydrogen atoms. Sander reports the preparation and characterization of trifluoro-1,3,5-tridehydrobenzene 2.¹ The characterization of this novel molecule is made through comparison with computed IR spectra.

2 is prepared by flash vapor pyrolysis of 1,3,5-triiodo-2,4,6-trifluorobenezene
and then trapping the products in a low temperature matrix. Sander identifies five IR peaks of a product he believes is 2. These IR frequencies are listed in Table 1.

Table 1. Experimental and computed^a IR frequencies (cm^-1) and relative intensities of 2.

^aUBLYP/cc-pVTZ. ^bTransition state.

In order to confirm that this IR spectra comes from 2, Sander computed the structure and IR frequencies of both 1 and 2. The ²A₁ structure of 1 had been studied previously², but what had gone unnoticed is that another structure is possible, the ²B₂ state. These two states differ in the separation between C₁ and C₃. When the distance is short, the SOMO is of a₁ symmetry and this orbital has bonding character between these two carbon centers, giving rise to the ²A₁ state (1a). As the distance gets longer between C₁ and C₃, a b₂ orbital, having antibonding character between C₁and C₃, becomes lower in energy than the a₁ orbital, so that the structure is ²B₂ (1b). The UBLYP/cc-pVTZ optimized structures are shown in Figure 1. 1a is 2-3 kcal mol^-1 lower in energy than 1b. Furthermore, 1b has one imaginary frequency and is not a local energy minimum. Sander also optimized the structures of 2a and 2b¸ finding little effect due to the fluorine substitution.

1a

1b

Figure 1. UBLYP/cc-pVTZ optimized structures of 1a (²A₁) and 1b (²B₁).

The computed IR frequencies are listed in Table 1. The computed frequencies (and their relative intensities) of 2a match up strikingly well with those of the experiment. Sander concludes that 2a has in fact been prepared and characterized.

References

(1) Venkataramani, S.; Winkler, M.; Sander, W., "Trifluoro-1,3,5-tridehydrobenzene," Angew. Chem. Int. Ed. 2007, 46, 4888-4893, DOI: 10.1002/anie.200700536

(2) Cristian, A. M. C.; Shao, Y.; Krylov, A. I., "Bonding Patterns in Benzene Triradicals from Structural, Spectroscopic, and Thermochemical Perspectives," J. Phys. Chem. A 2004, 108, 6581-6588, DOI: 10.1021/jp049007j.

InChI:

1: InChI=1/C6H3/c1-2-4-6-5-3-1/h1,4-5H
2: InChI=1/C6F3/c7-4-1-5(8)3-6(9)2-4

Kass has once again uncovered a simple system that challenges our notions of basic chemical concepts. It is a well accepted notion that the most acidic proton of all of the amino acids is the carboxylic acid one. However, acidities are strongly influenced by the solvent, and the absence of solvent in the gas phase can dramatically alter things.

Kass and co-workers examined the gas-phase acidity of cysteine with computational and
experimental techniques.¹ The lowest energy conformer of cysteine is 1a, characterized by having three intramolecular hydrogen bonds (Figure 1). The next lowest conformer, 1b, has only two intramolecular hydrogen bonds and is 1.5 kcal mol^-1 higher in energy at G3B3.

1a xyz	1b xyz

Figure 1. B3LYP/aug-cc-pVDZ optimized structures of cysteine 1.¹

They optimized a number of different configurations of the conjugate base of cysteine: two conformers from the loss of the carboxylate proton (2a and 2b), two conformers from the loss of the thiol proton (2c and 2d), and one conformer from the loss of the thiol proton of the zwitterion (2e). These structures are shown in Figure 2 along with their relative energies. All of these structures possess two intramolecular hydrogen bonds.

2a (3.1) xyz	2b (3.4) xyz
2c (0.0) xyz	2d (5.1) xyz
2e (10.1) xyz

Figure 2. B3LYP/aug-cc-pVDZ optimized structures of the conjugate base of cysteine 2. Relative energies (kcal mol^-1) in parenthesis computed at G3B3.¹

The gas phase acidity of carboxylic acids is greater than thiols; the deprotonation energy of propanoic acid (CH₃CH₂CO₂H) is 347.7 kcal mol^-1 at G3B3 (347.2 expt.²), about 6 kcal mol^-1 less than that of ethanethiol (CH₂CH₂SH: 355.0 at G3B3 and 354.2 expt.²). However, the computations indicate that 2c is the lowest energy structure of deprotonated cysteine, and 2c comes about by loss of the thiol proton! Te lowest energy cysteine conjugate base from loss of the carboxylate proton is 1a, which is 3.1 kcal mol^-1 higher in energy. Apparently, the hydrogen bonding network in 2c is quite favorable, able to make up for the inherent favorability of a carboxylate over a thiolate anion.

The G3B3 computed deprotonation energy of cysteine is 333.3 kcal mol^-1 (for removal of the thiol proton). Kass determined the deprotonation energy of cysteine using a kinetic and a thermodynamic method. The kinetic method gives a value of 332.9 ± 3.3 kcal mol^-1­, while the thermodynamic method gives 334.4 ± 3.3 kcal mol^-1­. These are in fine agreement with the computed value.

This study ably demonstrates the dramatic role that solvent can play in determining molecular properties. Kass titled the article “Are carboxyl groups the most acidic sites in amino acids?” and answers with “no” – in the gas phase the thiol group is more acidic. He ends the article with an indication that the alcohol of tyrosine may be competitive in acidity with its carboxylic group, too.

References

(1) Tian, Z.; Pawlow, A.; Poutsma, J. C.; Kass, S. R., "Are Carboxyl Groups the Most Acidic Sites in Amino Acids? Gas-Phase Acidity, H/D Exchange Experiments, and Computations on Cysteine and Its Conjugate Base," J. Am. Chem. Soc., 2007, 129, 5403-5407, DOI: 10.1021/ja0666194.

(2) NIST, NIST Chemistry WebBook, 2005, http://webbook.nist.gov/.

InChIs

1: InChI=1/C3H7NO2S/c4-2(1-7)3(5)6/h2,7H,1,4H2,(H,5,6)

Here’s one more nice application of computationally-derived NMR chemical shifts towards solving a structure. Fattarusso and co-workers¹ identified a component of wormwood called artarborol. COSY and ROESY experiments allowed for deducing four possible diasereomeric structures of artarborol, 1-4.

They then took two computational approaches towards resolving the structure. First, they performed an MM search for low energy conformers of 1-4. These conformers were then screened for those having a dihedral angle of around 90° for the C-8 and C-9 protons, due to a low couple constant for between these protons. Only conformers of 1 and 3 satisfied this criterion. An intense couple of the H-1 and H-5 protons indicated a transannular arrangement, and only conformers of 1 satisfy this criterion.

The second computational approach was to optimize some of the low energy conformers of 1 and 3 at mPW1PW91/6-31G(d,p) and compute their ¹³C chemical shifts. The five low energy conformers, two of 1 and three of 3, are shown in Figure 1. The resulting chemical shifts were averaged according to a Boltzmann distribution. These computed chemical shifts were then fit against the experimental values. The correlation factor for the computed shifts for 1 (r²=0.9997) was much better than that of 3 (r²=0.9713). The average deviation of the chemical shifts (after being corrected using the fitting procedure from the above correlation) was only 0.8ppm for 1 but 2ppm for 3. They therefore conclude that the structure of artarborol is 1.

1a xyz	1b xyz
3a xyz	3b xyz	3c xyz

Figure 1. mPW1PW91 optimized conformations of possible artarborol diasteromers.¹

References

(1) Fattorusso, C.; Stendardo, E.; Appendino, G.; Fattorusso, E.; Luciano, P.; Romano, A.; Taglialatela-Scafati, O., "Artarborol, a nor-Caryophyllane Sesquiterpene Alcohol from Artemisia arborescens. Stereostructure Assignment through Concurrence of NMR Data and Computational Analysis," Org. Lett., 2007, 9, 2377-2380, DOI: 10.1021/ol070803s.

(2) I thank Professor Ernesto Fattorusso for supplying me with the optimized coordinates of these compounds.

InChI

1: InChI=1/C14H24O2/c1-13(2)8-9-10(13)6-7-14(3)12(16-14)5-4-11(9)15/h9-12,15H,4-8H2,1-3H3/t9-,10-,11-,12-,14+/m0/s1

Just a short update here. In Chapter 5.1.2 we discuss nucleophilic substitution at heteroatoms. Unlike the paradigmatic case for substitution at carbon, which proceeds via the S_N2 mechanism. Nucleophilic substitution at second-row atoms (S, Si, P) appears to follow an addition-elimination pathway. Bickelhaupt¹ now adds a more thorough computational examination of nucleophilic substitution at phosphorus. He looked at a few identity reactions involving tricoordinate P, namely

X^– + PH₂X → PH₂X + X^–

X^– + PF₂X → PF₂X + X^–

X^– + PCl₂X → PCl₂X + X^–

where X is chloride or hydroxide. In all cases the only critical point located on the potential energy surface is for a tetracoordinate intermediate. Shown in Figure 1 are the intermediates for the reaction OH^– + PH₂OH and Cl^– + PCl₃. This result is consistent with the studies of nucleophilic substitution at sulfur and silicon.

(a) xyz file

(b) xyz file

Figure 1. OLYP/TZ2P optimized intermediate for the reaction (a) OH^– + PH₂OH
and (b) Cl^– + PCl₃.

References:

(1) vanBochove, M. A.; Swart, M.; Bickelhaupt, F. M., "Nucleophilic Substitution at Phosphorus (S_N2@P): Disappearance and Reappearance of Reaction Barriers," J. Am. Chem. Soc. 2006, 128, 10738-10744, DOI: 10.1021/ja0606529

I just ran across a nice summary article by Peter Schreiner¹ detailing the recent spate of articles describing problems with many DFT methods, especially the ubiquitous B3LYP functional. This article covers essentially the same ground as my previous post Problems with DFT.

References

(1) Schreiner, P. R., “Relative Energy Computations with Approximate Density Functional Theory – A Caveat!,” Angew. Chem. Int. Ed., 2007, 46, 4217-4219, DOI: 10.1002/anie.200700386.

Another three applications of computed NMR chemical shifts towards structure identification have appeared, dealing with carbohydrates and natural products.

Prediction of NMR Signals of Carbohydrates

The study by Cramer and Hoye¹ investigates identification of diastereomers with NMR, in particular, identification of cis and trans isomers of 2-methyl- (1), 3-methyl- (2), and 4-methylcyclohexanol (3). The study discusses the ability of different DFT methods to predict the chemical shifts of these alcohols in regard to distinguishing their different configurations. An interesting twist is that they have developed a functional specifically suited to predict proton chemical shifts and a second functional specifically for predicting carbon chemical shifts.²

The approach they take was first to optimize the six different conformations for each diastereomer including solvent (chloroform). They chose to optimize the structures at B3LYP/6-311+G(2d,p) with PCM. The six conformers (notice the axial/equatorial relationships, along with the position of the alcohol hydrogen) of 1c are presented in Figure 1. Chemical shifts were then obtained with a number of different methods, weighting them according to a Boltzmann distribution.

0.0 xyz	0.20 xyz	0.73 xyz
1.23 xyz	1.56 xyz	1.85 xyz

Figure 1. PCM/B3LYP/6-311+G(2d,p) optimized structures of the conformers of 1c. Relative energies (kcal mol^-1) are listed for each isomer.

Now a brief digression into how they developed their modified functional.² They define the exchange-correlation functional (see Chapter 1.3.1 of my book – or many other computational chemistry books!) as

      E_xc = P₂E_x(HF) + P₃ΔE_x(B) + P₄E_x(LSDA) + P₅ΔE_c(LYP) + P₆E_c(LSDA)

where the Ps are parameters to be fit and E_x(HF) is the Hartree-Fock exchange energy, ΔE_x(B) is the Becke gradient correction to the local spin-density approximation (LSDA), E_x(LSDA) is the exchange energy, ΔE_c(LYP) is the Lee-Yang-Parr correction to the LSDA correlation energy, and E_c(LSDA) is the LSDA correlation energy. Chemical shifts were computed for proton and carbon, and the parameters P were adjusted (between 0 and 1) to minimize the error in the predicted chemical shifts from the experimental values. A total of 43 different molecules were used for this fitting procedure. The values of the parameters are given for the carbon functional (WC04), the proton functional (WP04) and B3LYP (as a reference) in Table 1. Note that there is substantial difference in the values of the parameter among these three different functionals.

Table 1. Values of the parameters P for the functionals WC04, WP04, and B3LYP.

Now, the computed proton and carbon chemical shifts using 4 different functions (B3LYP, PBE1, MP04, and WC04) for 1-3 were compared with the experiment values. This comparison was made in a number of different ways, but perhaps most compellingly by looking at the correlation coefficient of the computed shifts compared with the experimental shifts. This was done for each diastereomer, i.e. the computed shifts for 2c and 2t were compared with the experimental shifts of both 2c and 2t. If the functional works well, the correlation between the computed and experimental chemical shifts of 2c (and 2t) should be near unity, while the correlation between the computed shifts of 2c and the experimental shifts of 2t should be dramatically smaller than one. This is in fact the case for all three functionals. The results are shown in Table 2 for B3LYP and WP04, with the later performing slightly better. The results for the carbon shifts are less satisfactory; the correlation coefficients are roughly the same for all comparisons with B3LYP and PBE1, and WC04 is only slightly improved.
Nonetheless, the study clearly demonstrates the ability of DFT-computed proton chemical shifts to discriminate between diasteromers.

Table 2. Correlation coefficients between the computed and experimental proton chemical shifts.^a

	2ccomp (1.06) xyz	2tcomp (0.0) xyz
2cexp   2texp	0.9971 0.9985 0.8167 0.8098	0.8334 0.9050 0.9957 0.9843
	3c (0.0) xyz	3t (0.63) xyz
3cexp   3texp	0.9950 0.9899 0.8856 0.9310	0.8763 0.8717 0.9990 0.9979
	4c (0.54) xyz	4t (0.0) xyz
4cexp   4texp	0.9993 0.9975 0.8744 0.8675	0.8335 0.9279 0.9983 0.9938

^aPCM/B3LYP/6-311+G(2d,p)//PCM/ B3LYP/6-31G(d) in regular type and PCM/WP04/6-311+G(2d,p)//PCM/ B3LYP/6-31G(d) in italic type. Relative energy (kcal mol^-1) of the most favorable conformer of each diastereomer is given in parenthesis.

Predicting NMR of Natural Products

Bagno has a long-standing interest in ab initio prediction of NMR. In a recent article, his group takes on the prediction of a number of complex natural products.³ As a benchmark, they first calculated the NMR spectra of strychnine (4) and compare it with its experimental spectrum. The optimized PBE1PBE/6-31G(d,p) geometry of 4 is drawn in Figure 2. The correlation between the computed NMR chemical shifts for both ¹H and ¹³C is quite good, as seen in Table 3. The corrected mean average errors are all very small, but Bagno does point out that four pairs of proton chemical shifts and three pairs of carbon chemical shifts are misordered.

Strychnine 4

Figure 2. PBE1PBE/6-31G(d,p) geometry of strychnine 4.³

Table 3. Correlation coefficient and corrected mean average error
(CMAE) between the computed and experiment chemical shifts of 4.

The study of the sesquiterpene carianlactone (5) demonstrates the importance of including solvent in the NMR computation. The optimized B3LYP/6-31G(d,p) geometry of 5 is shown in Figure 3, and the results of the comparison of the computed and experimental chemical are listed in Table 4. The correlation coefficient is unacceptable when the x-ray structure is used. The agreement improves when the gas phase optimized geometry is employed, but the coefficient is still too far from unity. However, optimization using PCM (with the solvent as pyridine to match experiments) and then computing the NMR chemical shifts in this reaction field provides quite acceptable agreement between the computed and experimental chemical shifts.

Corianlactone 5

Figure 3. B3LYP/6-31G(d,p) geometry of carianlactone 5.³

Table 4. Correlation coefficient and corrected mean average error (CMAE) between
the computed and experiment chemical shifts of 5.

Lastly, Bagno took on the challenging structure of the natural product first identified as boletunone B (6a).⁴ Shortly thereafter, Steglich reinterpreted the spectrum and gave the compound the name isocyclocalopin A (6b).⁵ A key component of the revised structure was based on the δ 0.97 ppm signal that they assigned to a methyl above the enone group, noting that no methyl in 6a should have such a high field shift.

Bagno optimized the structures of 6a and 6b at B3LYP/6-31G(d,p), shown in Figure 4. The NMR spectra for 6a and 6b were computed with PCM (modeling DMSO as the solvent). The correlation coefficients and CMAE are much better for the 6b model than for the 6a model., supporting the reassigned structure. However, the computed chemical shift for the protons of the key methyl group in question are nearly identical in the two proposed structures: 1.08 ppm in 6a and 1.02 ppm in 6b. Nonetheless, the computed chemical shifts and coupling constants of 6b are a better fit with the experiment than those of 6a.

boletunone B 6a	isocyclocalopin A 6b

Figure 4. B3LYP/6-31G(d,p) geometry of the proposed structures of Boletunone B, 6a and 6b.³

Table 5. Correlation coefficient and corrected mean average error (CMAE) between the computed (B3LYP/6-31G(d,p) + PCM) and experiment chemical shifts of 6a and 6b.

In a similar vein, Nicolaou and Frederick has examined the somewhat controversial structure of maitotxin.⁶ For the sake of brevity, I will not draw out the structure of maitotxin; the interested reader should check out its entry in wikipedia. The structure of maitotoxin has been extensively studied, but in 2006, Gallimore and Spencer⁷ questioned the stereochemistry of the J/K ring juncture. A fragment of maitotoxin that has the previously proposed stetreochemistry is 7. Gallimore and Spencer argued for a reversed stereochemistry at this juncture (8), one that would be more consistent with the biochemical synthesis of the maitotoxin. Nicolaou noted that reversing this stereochemistry would lead to other stereochemical changes in order for the structure to be consistent with the NMR spectrum. Their alternative is given as 9.

7

8

9

Nicolaou and Freferick computed ¹³C NMR of the three proposed fragments 7-9 at B3LYP/6-31G*; unfortunately they do not provide the coordinates. They benchmark this method against brevetoxin B, where the average error is 1.24 ppm, but they provide no error analysis – particularly no regression so that corrected chemical shift data might be employed. The best agreement between the computed and experimental chemical shifts is for 7, with average difference of 2.01 ppm. The differences are 2.85 ppm for 8 and 2.42 ppm for 9. These computations support the original structure of maitotoxin. The Curious Wavefunction blog discusses this topic, with an emphasis on the possible biochemical implication.

References

(1) Wiitala, K. W.; Al-Rashid, Z. F.; Dvornikovs, V.; Hoye, T. R.; Cramer, C. J., "Evaluation of Various DFT Protocols for Computing ¹H and ¹³C Chemical Shifts to Distinguish Stereoisomers: Diastereomeric 2-, 3-, and 4-Methylcyclohexanols as a Test Set," J. Phys. Org. Chem. 2007, 20, 345-354, DOI: 10.1002/poc.1151

(2) Wiitala, K. W.; Hoye, T. R.; Cramer, C. J., "Hybrid Density Functional Methods Empirically Optimized for the Computation of ¹³C and ¹H Chemical Shifts in Chloroform Solution," J. Chem. Theory Comput. 2006, 2, 1085-1092, DOI: 10.1021/ct6001016

(3) Bagno, A.; Rastrelli, F.; Saielli, G., "Toward the Complete Prediction of the ¹H and ¹³C NMR Spectra of Complex Organic Molecules by DFT Methods: Application to Natural Substances," Chem. Eur. J. 2006, 12, 5514-5525, DOI: 10.1002/chem.200501583

(4) Kim, W. G.; Kim, J. W.; Ryoo, I. J.; Kim, J. P.; Kim, Y. H.; Yoo, I. D., "Boletunones A and B, Highly Functionalized Novel Sesquiterpenes from Boletus calopus," Org. Lett. 2004, 6, 823-826, DOI: 10.1021/ol049953i

(5) Steglich, W.; Hellwig, V., "Revision of the Structures Assigned to the Fungal Metabolites Boletunones A and B," Org. Lett. 2004, 6, 3175-3177, DOI: 10.1021/ol048724t.

(6) Nicolaou, K. C.; Frederick, M. O., "On the Structure of Maitotoxin," Angew. Chem. Int. Ed., 2007, 46, 5278-5282, DOI: 10.1002/anie.200604656.

(7) Gallimore, A. R.; Spencer, J. B., "Stereochemical Uniformity in Marine Polyether Ladders – Implications for the Biosynthesis and Structure of Maitotoxin," Angew. Chem. Int. Ed. 2006, 45, 4406-4413, DOI: 10.1002/anie.200504284.

InChI

1: InChI=1/C7H14O/c1-6-4-2-3-5-7(6)8/h6-8H,2-5H2,1H3
2: InChI=1/C7H14O/c1-6-3-2-4-7(8)5-6/h6-8H,2-5H2,1H3
3: InChI=1/C7H14O/c1-6-2-4-7(8)5-3-6/h6-8H,2-5H2,1H3
4: InChI=1/C21H22N2O2/c24-18-10-16-19-13-9-17-21(6-7-22(17)11-12(13)5-8-25-16)14-3-1-2-4-15(14)23(18)20(19)21/h1-5,13,16-17,19-20H,6-11H
5: InChI=1/C14H14O6/c1-12-2-6(15)8-13(4-18-13)9-10(19-9)14(8,20-12)7-5(12)3-17-11(7)16/h5,7-10H,2-4H2,1H3/t5-,7-,8?,9+,10+,12+,13?,14-/m1/s1
6a: InChI=1/C15H20O6/c1-7-4-5-14(3)12(17)9-8(2)6-20-15(14,11(7)16)21-10(9)13(18)19/h4,8-10,12,17H,5-6H2,1-3H3,(H,18,19)/t8-,9+,10+,12+,14-,15-/m1/s1
6b: InChI=1/C15H20O6/c1-7-4-5-15(12(17)10(7)16)9-8(2)6-20-14(15,3)21-11(9)13(18)19/h4,8-9,11-12,17H,5-6H2,1-3H3,(H,18,19)/t8?,9-,11?,12+,14-,15-/m1/s1