Hydrogen Bond Basicity Prediction for Medicinal Chemistry Design

Subscriber access provided by ORTA DOGU TEKNIK UNIVERSITESI KUTUPHANESI

Article

Hydrogen bond basicity prediction for medicinal chemistry design Peter W. Kenny, Carlos A. Montanari, Igor M. Prokopczyk, Jean F. R. Ribeiro, and Geraldo Rodrigues Sartori J. Med. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jmedchem.5b01946 • Publication Date (Web): 12 Feb 2016 Downloaded from http://pubs.acs.org on February 13, 2016

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Journal of Medicinal Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Journal of Medicinal Chemistry

Hydrogen bond basicity prediction for medicinal chemistry design Peter W. Kenny*, Carlos A. Montanari, Igor M. Prokopczyk, Jean F. R. Ribeiro, Geraldo Rodrigues Sartori

Grupo de Estudos em Química Medicinal – NEQUIMED, Instituto de Química de São Carlos – Universidade de São Paulo, Av. Trabalhador Sancarlense, 400, 13560-590 São Carlos, SP, Brazil

ABSTRACT

Hydrogen bonding is discussed in the context of medicinal chemistry design. Minimized molecular electrostatic potential (Vmin) is shown to be an effective predictor of hydrogen bond basicity (pKBHX) and predictive models are presented for a number of hydrogen bond acceptor types relevant to medicinal chemistry. The problems posed by the presence of non-equivalent hydrogen bond acceptor sites in molecular structures are addressed by using non-linear regression to fit measured pKBHX to calculated Vmin. Predictions are made for hydrogen bond basicity of fluorine in situations where relevant experimental measurements are not available. It is shown how predicted pKBHX can be used to provide insight into the nature of bioisosterism and

ACS Paragon Plus Environment

1


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 44

to profile heterocycles. Examples of pKBHX prediction for molecular structures with multiple, non-equivalent hydrogen bond acceptors are presented.

INTRODUCTION

Hydrogen bonding1-6 is a key element of molecular recognition7,8 and is implicated in diverse physicochemical phenomena such as crystal packing,9-11 DNA base-pairing,12,13 protein folding14-18 and enzyme specificity.19 The cohesiveness of liquid water that drives hydrophobic association20,21 in aqueous media is a consequence of cooperative hydrogen bonds between water molecules. In the pharmaceutical context, hydrogen bonding influences solubility of drugs, both in water and lipid, and the affinity with which they associate with their targets. Partition coefficients,22,23 especially when measured for the 1-octanol/water system, are widely used in drug discovery and differences between values measured for a compound in different systems (e.g. cyclohexane/water) reflect the hydrogen bonding characteristics of the compound.24-30

Hydrogen-bond (HB) strength can be quantified by the stability of the 1:1 complex between an HB donor and an HB acceptor in a non-polar solvent such as tetrachloromethane or 1,1,1-trichloroethane.3,31,32,33,34 The 1:1 complex stability measured using a reference HB donor such as 4-fluorophenol31-33 or 4-nitrophenol34 can be used to define scales for HB basicity and a reference HB acceptor such as 1-methylpyrrolidone can be used to quantify HB acidity in an analogous manner.34 Measured HB basicity (and acidity) can be used to guide molecular design although there are limitations to how the measurements can be used. Firstly, measurements made for compounds which have non-equivalent HB acceptors cannot, in general, be used to characterize the different HB acceptor sites for a molecular structure although in some cases35 the individual isomeric 1:1 complexes can be observed. Secondly, in polar solvents such as water


2

Page 3 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


or in ligand-protein complexes, formation of multiple interactions can perturb HB basicity and acidity of individual acceptors and donors. This is a particular issue for carbonyl oxygen atoms, which are typically associated with two HB acceptor sites, and hydroxyl groups for which an HB donor and acceptor are in close proximity.29,36 Outside narrowly-defined structural series, pKa is not an effective predictor of HB acidity or basicity.33,34 This study focuses on the use of molecular electrostatic potential (MEP) minima37 to interpret and predict HB basicity. MEP minima are points in space where the electric field strength vanishes and the value (Vmin) of MEP at the minimum has been shown to be predictive of HB basicity.37,38 Although HB donors lack analogous MEP maxima, HB acidity can be predicted in an analogous manner using the Vα(r) descriptor which is defined as MEP calculated on the geometric extension of the covalent bond to the HB donor hydrogen.36 MEP calculated on molecular surfaces has also been used39 for prediction of HB basicity and acidity although it has not been shown that these MEP values are optimal for this purpose. Polarization charge density has been shown to be a useful and effective predictor of both enthalpy and entropy changes associated with hydrogen bond formation.40 HB strength can be predicted by calculating energies for the HB donor, HB acceptor and complex although high-level calculations are usually necessary to obtain useful results.41 Furthermore, the need to sample and minimize the energies of multiple orientations of the HB donor and acceptor typically imposes significant computational costs. It may be helpful to articulate what is meant by the term ‘design’ since one objective of this study is to demystify some of the computational methods used in medicinal chemistry. Molecular design may be defined42 as control of behavior of compounds and materials by manipulation of molecular properties. It is not generally feasible to predict all relevant chemical behavior from


3


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 44

molecular structure with sufficient accuracy to enable what can be termed36 'prediction-driven molecular design' and practical design typically requires that some experimental measurements be made. Molecular design is not only about making predictions and the focus of hypothesisdriven molecular design36,42 is generating the knowledge that is required for making informed decisions. The principal challenge for hypothesis-driven molecular design is to make it as systematic, objective and efficient as possible and there is a degree of overlap with statistical molecular design.43 Much medicinal chemistry design, especially in lead optimization, focuses on the effects (e.g. on affinity and aqueous solubility) of structural modifications. In cheminformatic terms this is equivalent to saying that biological activity and physicochemical properties may be perceived in terms of structural relationships between compounds.42,44 Molecular interactions7,8 are of great importance in medicinal chemistry because much of the behavior of a compound is determined by the interactions of its molecules with the environments in which they exist. Although much is known about geometry of molecular interactions, exploiting this knowledge to predict affinity remains challenging. A particular problem is that the contribution of an intermolecular contact to affinity is not, in general, an experimental observable.45 Individual interactions differ in their geometric preferences and, as the number of interactions increases, it becomes progressively more difficult to simultaneously accommodate these preferences. This is the essence of the molecular complexity model46 of Hann et al and these effects can also be considered in terms of non-additivity of interactions. Zhou and Gilson emphasize the (frequently disregarded) arbitrary nature of the standard state when interpreting molecular recognition phenomena and also highlight pitfalls associated with attempting to distribute affinity between individual molecular recognition elements of the ligand structure.47


4

Page 5 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


In biological systems, molecular recognition occurs in aqueous environments and this greatly complicates interpretation of molecular interactions.19 Hydrogen bonding partners need to compensate each other for lost solvation and this suggests the existence of an optimal ligand charge distribution48 that will maximize affinity for a given protein charge distribution. In terms of hydrogen bonding, we can think of needing to balance36,39 the HB basicity of the acceptor with the HB acidity of the donor to get the most effective interaction and it may be useful to interpret binding enthalpy49 in these terms. It is commonly believed that HB formation between protein and ligand is of limited scope as a means for increasing affinity and it has been asserted that “a neutral - neutral hydrogen bond is worth less than 15-fold in binding”.50 We challenge this view on the grounds that it is based on a small body of data and deletion of a single HB donor or acceptor from a molecular structure has effects other than simply eliminating an intermolecular HB from the association complex. For example, of the atoms in a linker may be able to move more freely in the complex or make more effective hydrophobic contact with the protein once the ligand atom participating in the relevant HB has been eliminated. On a less speculative note, aza-substution of a pyridyl substituent in a cysteine protease inhibitor has been observed to result in increases in pIC50 of 1.9 and >2.2 respectively against cathepsins S and L2.51 One aspect of the drug designer’s job might be summarized as beating water at hydrogen bonding and the pKBHX value of 0.65 measured33 for water indicates that it is a relatively weak HB acceptor. It should also be noted that contact between polar and non-polar molecular surfaces is not inherently repulsive as illustrated by the observation52 that gas to hexadecane transfer is slightly more favorable for tetrahydrofuran (∆G° = −0.84 kcal/mol) than for cyclopentane (∆G° = − 0.72 kcal/mol). As such, it is questionable to invoke,7 “a dual character


5


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 6 of 44

of the weakly polar sulfonyl groups as a hydrogen bond acceptor and as a hydrophobic group” as a rationale for the existence of intermolecular contacts of this nature in crystal structures. One tactic employed in structure-based design is to identify53,54 unstable water molecules bound to protein that can be readily displaced by ligands but one might also attempt to identify ‘privileged’ arrangements of HB donors and acceptors that cannot easily be mimicked by clusters of water molecules. Much of the value of predicting HB basicity (and acidity) stems from helping medicinal chemists to think about consequences of structural modifications before these have been made or when relevant experimental data are lacking. In this study, we first show how Vmin can be used as a predictor of pKBHX and then use a number of examples to illustrate the scope for exploiting predicted HB basicity in medicinal chemistry design. We refer readers to a selection of reviews32,33 and articles55-63 in which hydrogen bonding is discussed in a medicinal chemistr context. RESULTS AND DISCUSSION Modelling HB basicity with Vmin Many medicinal chemists will be unfamiliar with the MEP minima used to model HB basicity so a good way to start this section is to note that these can be considered to be roughly equivalent to ‘lone pairs’. The Vmin value at the MEP minimum can be thought of as the ‘strength’, ‘intensity’ or ‘availability’ of the ‘lone pair’. Vmin is a more direct (and less arbitrary) measure than an atomic charge of what a hydrogen bond donor would ‘see’. MEP minima typically lie within the van der Waals molecular surface and computational chemists should be aware that these minima cannot, in general, be reproduced using atom-centered multipoles. When only a


6

Page 7 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


single MEP minimum is present in the molecular structure, HB basicity can be modelled38 according to a linear model with intercept A and slope B (1). pKBHX = A + B×Vmin

(1)

If two HB acceptor sites are present in the molecular structure, the observed association constant K is simply the sum of the association constants (K1 and K2) for the individual acceptors (2).33,38 K = K1 + K2

(2)

There are two simplifying assumptions that can sometimes be made in order to fit measured pKBHX when two HB acceptor sites are present in a molecular structure. Firstly, when the HB acceptors are equivalent (K1 = K2) the measured pKBHX can be corrected statistically and used for fitting as if only a single HB acceptor is present. Secondly, when one of the HB acceptors is likely to be much stronger than the other (e.g. oxazole), we can neglect the contribution of the weaker acceptor to HB basicity when fitting pKBHX to Vmin. In this scenario, the relevant Vmin values are useful for testing the validity of the assumptions. Oxygen, sulfur and fluorine atoms are typically associated with two or three MEP minima with comparable Vmin values that are not in general equivalent and it is not, in general, valid to statistically correct pKBHX in these situations. One solution to the problem of fitting measured pKBHX to comparable, non-equivalent Vmin is to use non-linear regression as values as exemplified by (3) in which V1 and V2 are two non-equivalent Vmin values. pKBHX = A + log10( 10B×V1 + 10B×V2)

(3)

Although (2) and (3) can be generalized to any number of HB acceptor sites, the pKBHX value measured for a compound with multiple, non-equivalent HB acceptors has no thermodynamic


7


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 8 of 44

significance.33 Furthermore, the sum of association constants for all isomeric 1:1 complexes is not directly relevant to the solvated state in which all HB acceptors and donors of the solute can interact simultaneously with solvent molecules. HB basicity measurements for compounds with non-equivalent acceptors are only useful if the association constants for individual acceptors can be quantified35 and this is not generally the case for compounds of interest to medicinal chemists. Predictive models usually represent the only practical means of quantifying HB basicity of individual acceptors in molecular structures of interest to medicinal chemists although measurements made for structural prototypes are absolutely essential for developing the predictive models. The first objective of this study was to explore the influence of atom type and computational protocol on the performance of Vmin as a predictor of HB basicity and we used a database of 364 measured pKBHX values to address these questions. Each of the protocols used in this study to calculate Vmin consists of a theoretical model and basis set of atom-based functions from which the electron density of the molecule can be constructed. Modelling interactions between electrons is the main challenge in quantum chemistry. The four theoretical models differ in how they achieve this and in their computational cost (which increases with the size of the molecule and number of atom-based functions in the basis set). Energy-minimization of molecular structures with quantum mechanical models is more demanding than calculating MEP and we have used a much less computationally expensive molecular mechanics model for this purpose. We have also created ADD_CENTRE for setting up the starting points for the minimization of MEP and source code is provided as supplementary information. A summary of the results of fitting pKBHX to Vmin for different atom types and computational protocols is presented in Table 1 and complete results are provided as supplemental material.


8

Page 9 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Summary statistics are listed for two protocols for each atom type in Table 1. These are the model derived from the least demanding computational protocol (HF/6-31G**; see computational details section) and the best model for the atom type as judged by root mean square error (RMSE). In cases where least demanding computational model was found to be optimal, summary statistics for the second best model are also given. For a given atom type, it is valid to use RMSE to rank models because each model is fit using two parameters to identical data. The standard deviation for measured pKBHX is also given for each atom type in Table 1 and this quantity indicates how well pKBHX is likely to be predicted by the mean value measured for the relevant atom type. It should be noted that the coefficient of determination (R2), commonly used as a measure of quality of linear models, cannot be used for non-linear models.64 The models can be used in a number of ways. We would recommend the use of Vmin calculated using the HF/6-31G** protocol (see computational details section) as a default for prediction of HB basicity since it is the least computationally demanding of the protocols that were evaluated and performs acceptably across a range of atom types. Plots of measured versus predicted pKBHX are shown for six models derived using this protocol are shown in Figure 1 and it should be apparent that the RMSE value (0.33; Table 1) for the amine nitrogen is comparable with the standard deviation (0.43; Table 1) for this atom type. Should it be necessary to make the most accurate predictions for a specific atom type then the protocol most appropriate to that atom type can be used. It must be stressed that it would not be valid to invoke these results in support of an assertion that one theoretical model was better than another. Hydrogen bond basicity of fluorine


9


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 10 of 44

Fluorine is of great interest in medicinal chemistry although its properties and behavior are sometimes considered to be enigmatic.65,66 For example, it is a weak HB acceptor when bound to carbon despite being the most electronegative element. However, this might be seen as simply illustrating the importance of charge distribution within, as opposed to between, atoms as a determinant of interaction potential. Measurements67 of pKBHX have been reported for seven saturated hydrocarbons with one or two fluoro substitutions. The value of pKBHX for 1 (0.48) is noteworthy and can be interpreted in terms of a through-space, repulsive interaction between the electrons associated with the fluorine atoms. The small size and limited range (0.75 log units) of the training data make it important to use the best protocol for calculating Vmin which in this case was MP2/6-311+G** (see computational details section). The fit of of pKBHX to Vmin is shown in Figure 2 and these results are consistent with what has been reported in the literature. The value (−0.5) of pKBHX predicted for 2 suggests that fluorine bonded to aromatic carbon will be, general, a weaker hydrogen bond acceptor when bonded to aliphatic carbon. Each fluorine atom in 3 is predicted (pKBHX = −0.6) to be a slightly weaker HB acceptor than the fluorine atom of 2, suggesting that the through-bond interaction between the fluorine atoms outweighs the through-space interaction. The fluorine atom of 2 is associated with two equivalent MEP minima above and below the molecular plane but the single MEP minimum associated each fluorine atom of 3 lies in the molecular plane. The Vmin calculations can be used to explore the effect of substituting fluorine for hydrogen and the predicted difference (0.8) in pKBHX between 4 and 5 on a per fluorine atom basis is comparable with the corresponding difference68 (0.6) between fluoromethylbenzene and difluoromethylbenzene measured at −2°C . All bioisosteres are not created equal


10

Page 11 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Bioisosterism69-72 is an important concept in medicinal chemistry and computed values of Vmin can provide insight into the nature of bioisosteric relationships. For example, Vmin and Vα(r) were used to compare guanine and adenine bioisosteres from the perspective of DNA basepairing.36 Meaningful results can sometimes even be obtained for charged systems and Vmin calculations provide a view of the bioisosteric relationship between carboxylate and tetrazole anions.36 In some cases, a bioisosteric relationship may be associated with systematic differences in physicochemical properties and tetrazoles appear to be more highly bound by plasma protein than the corresponding carboxylic acids despite being less lipophilic.73 The bioisosteric relationship between 1,3,4-oxadiazoles and 1,2,4-oxadiazoles has been examined from the perspective of physicochemical properties and Vmin was used to provide a rationale for experimental observations.57,58 Both studies noted that 1,3,4-oxadiazoles tend to be less lipophilic and more soluble in aqueous media than the corresponding 1,2,4-oxadiazoles.57,58 In some cases, lipophilicity prediction software was unable to distinguish regioisomers even when their measured log D values differed by over a unit. Predictions for HB basicity are presented in Figure 3 for oxadiazole regioisomers and some related compounds. Although prototypical structures were used for this generic study of these bioisosteres, structurally elaborated models can easily be used if it is necessary to characterize a bioisosteric relationship in specific structural context. The pKBHX values predicted for 8, 9 and 10 are consistent with the previous studies57,58 in that the nitrogen atoms of the 1,3,4-oxadiazole have greater HB basicity than their regioisomeric equivalents. The predicted pKBHX values for the tetrazole 11 suggest that its hydrogen bonding profile (and, by implication, its lipophilicity) will be intermediate between the profiles of 8 and 9. The 1,2,3-oxadiazoles and 1,2,5-oxadiazoles (12, 13, 14) are predicted to be even weaker HB acceptors than their 1,2,4-oxadiazole


11


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 44

equivalents although the tetrazole bioisostere 15, linked at N1 in this case rather than N2, is predicted to have comparable HB basicity to the 1,3,4-oxadiazole 8. Oxazoles and isoxazoles such as 16 and 18 can be mimicked by triazoles (17 and 19) for which the predicted pKBHX values illustrate how triazole isomer and linking position are likely to influence HB basicity. HB basicity measurements that could have been used to compare 1,2,4-oxadiazoles with their 1,3,4-oxadiazole regioisomers do not appear to have been reported so the calculated Vmin values were particularly useful in these studies.57,58 Predicted HB basicity can provide useful information even if measurements are available when non-equivalent HB acceptors are present in the molecular structure. The contributions of individual HB acceptors to the overall formation constant can only be measured in certain circumstances and large differences in HB basicity between individual acceptors make measurement more difficult. The oxadiazole bioisostere studies57,58 highlight a problem faced by users of physicochemical property prediction software. In lead optimization it is particularly important that the effects of structural changes be predicted accurately. Typically, data used to train models are not freely available which makes it impossible to know whether or not chemotypes of interest are adequately represented in training data. Quoted measures of model quality may simply be irrelevant for some chemotypes but users of models are not usually in a position to know if this is the case for specific chemotypes of interest. The oxadiazole bioisostere studies57,58 also show how HB basicity predictions can be used to challenge (and possibly even correct) predictive models. When attempting to control lipophilicity in lead optimization projects it may be helpful to draw a distinction between an increase in HB basicity and an increase in the number of HB acceptors.


12

Page 13 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Heterocyclic profiles and through-space interactions Molecular recognition characteristics and physicochemical properties of heterocycles are heavily influenced by HB basicity and acidity. For example, furan (logP = 1.34)74 is more lipophilic than tetrahydrofuran (logP = 0.46)74 but benzene (logP = 2.13)74 is less lipophilic than cyclohexane (logP = 3.44).74 This apparent paradox is easily resolved once it is recognized that the HB basicity of tetrahydrofuran (pKBHX = 1.28)33 is greater than that of furan (pKBHX = −0.40).33 Predicted hydrogen bonding characteristics can be used to profile75 heterocycles when relevant physicochemical measurements are unavailable, as can be the case when the heterocycle is uncommon, or has an unusual substitution pattern. The heterocycles discussed in this section are shown in Figure 4. One difficulty likely to be encountered when attempting to enhance HB basicity of heteroaromatic nitrogen is that structural modifications that make a nitrogen atom a better HB acceptor will also tend to make it a better proton acceptor with the result that loss of neutral form nullifies any gain in HB basicity. For example, N-methylimidazole (pKBHX = 2.72; pKa = 7.25)33,76 and 4-dimethylaminopyridine (pKBHX = 2.80; pKa = 9.70) 33,76 are of comparable HB basicity despite differing significantly in proton basicity. An inference that might be drawn from this observation is that a strong, neutral HB acceptor based on aromatic nitrogen can be more easily accommodated in a five-membered ring than in a six-membered ring. The observation that pyridazine (pKBHX = 1.95; pKa = 2.24)33,76 and pyridine (pKBHX = 1.86; pKa = 5.22) 33,76 are of similar HB basicity despite differing significantly in pKa hints at how a strong HB acceptor might be accommodated in a six-membered aromatic ring without losing it to protonation. While the pKBHX value (2.6) predicted for N1 of 20 approaches that of 4-


13


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 44

dimethylaminopyridine, its pKa is expected to be similar to the value of 6.8 reported for 4aminopyridazine.77 The apparently anomalous HB basicity of pyridazine can be interpreted as due to a through-space interaction and may also be seen as a manifestation of the α effect.78 The results obtained for pyridazine raise the question of what would be observed for the more obscure 1,2,3-triazine79 (21) and the pKBHX values predicted for its nitrogen atoms are all significantly greater than the measured value (0.38; statistically corrected)33 for a single nitrogen atom in the isomeric 1,3,5-triazine. Differences in distribution of HB basicity across the three ring nitrogen atoms predicted for 21, 22 and 23 may be of interest and it is instructive to compare these HB basicity profiles with that of the tetrazole 15. The naphthyridines provide another illustration of how predicted HB basicity can be used to profile heterocycles. One would expect aza-substitution of one ring in a fused heterocyclic system to lead to a reduction in the HB basicity of nitrogen in the other ring and predicted pKBHX values for five of the isomers are indeed 0.3 to 0.7 units lower than the measured values for quinoline (1.89)33 and isoquinoline (1.94).33 The predicted pKBHX (2.5) for the nitrogen atoms of 27 suggests that the HB basicity of this compound will approach that of 4dimethylaminopyridine (pKBHX = 2.80)33 even though it is less basic (pKa = 3.39)80 than pyridine. In this system, the effects of through-space interactions between the HB acceptors of 27 outweigh effects of through-bond interactions. As noted in the introduction, molecular structures that can present arrangements of hydrogenbonding groups that cannot easily be mimicked by clusters of water molecules are of potential interest in drug design. For example, we might conjecture that it would be difficult for a cluster of water molecules to mimic the adjacent, aligned HB acceptors of pyridazine, 21 or 27, especially if an additional restriction that HB donors be absent is also imposed. This line of


14

Page 15 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


thinking can be explored in a number of ways. Firstly, one might use these structures as docking probes81 to test complementarity with respect to concave regions of the protein molecular surface. Secondly, compounds capable of presenting these molecular recognition elements might be included in generic fragment screening libraries82 or even designed83 specifically as fragments. It is possible that 20, 22, 23 and 27 would be suitable for crystallographic fragment screening.84 Hydrogen bond basicity predictions for structurally elaborated species The predicted pKBHX values presented so far in this study were for structurally prototypical compounds and, in this section, we show how Vmin calculations can be used applied to molecular structures of the complexity that medicinal chemists typically encounter. HB basicity can often be assessed by consulting compilations33,34 of measured values just as the pKa for an ionizable center in a complex molecular structure can be estimated from the value measured for a structural prototype (e.g. morpholine). However, compilations of measured values are less useful if substitution patterns are complex or HB acceptors are part of an extended π-system. Predicted pKBHX values are presented for five structurally-related phosphatidylinositol 3-kinase inhibitors63 in Figure 5. The nitrogen atoms in the benzotriazole and pyrazine rings are predicted to be relatively weak HB acceptors. This reflects the general tendency of aza-substitution weaken HB basicity for individual acceptors as well as the likely presence of an intramolecular hydrogen bond between the amino group and N2 of the benzotriazole ring. The effects on lipophilicity of modulating HB basicity of the nitrogen atoms in the non-fused, five-membered ring can be observed and the logD7.4 measured for 33 (imidazole) is 0.5 units lower than for 31 (pyrazole). While aza-substitution of 30 to give 32 leads to a marginal increase in lipophilicity, 34, the aza-


15


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 44

derivative of 31 is 0.9 units less lipophilic than the parent compound. The effects of these azasubstitutions on inhibition of PIK3α reflect predicted pKBHX and the relevant changes in pIC50 are [30→32] (∆pIC50 = −0.1) and [31→34] (∆pIC50 = −0.6 ). In contrast, inhibitory activity against KDR is more sensitive to aza-substitution and the corresponding changes in pIC50 are [30→32] (∆pIC50 = −0.9) and [31→34] (∆pIC50 = −2.0 ) for this enzyme.63 The amide carbonyl oxygen atom is predicted to be the strongest HB acceptor for each of five compounds. The values of pKBHX predicted for 30 and 32 indicate an interaction between amide and a nitrogen atom of the heteroaromatic substituent at C4 of the piperidine since the corresponding pKBHX predictions are greater for 30, 32 and 33. There is precedent for interactions like this in that C4-substitution of a piperidine with fluorine leads to a decrease in pKa of 1.9.85 Solid state chemistry86 of active pharmaceutical ingredients is an important determinant of solubility and dissolution rate and the existence of polymorphs can complicate development, and even manufacture, of drugs.87 Hydrogen bonding in the solid state is the focus of Etter’s rules,9 the first of which states that, “all good proton donors and acceptors are used in hydrogen bonding”. A recent study88 of polymorphism of theophylline prompted the question as to how predicted HB basicity and acidity might provide insight into molecular interactions in the solid state. One assumption made in the study was that the strongest acceptor in theophylline is N9 although HB basicity predictions (Figure 6) suggest that it will actually be a weaker acceptor than either of the carbonyl oxygen atoms. None of the HB acceptors of theophylline are likely to be especially strong indicating that the rings are mutually electron-withdrawing. The pKBHX value (0.5) predicted for of N9 is comparable with the statistically corrected value (0.38)33


16

Page 17 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


measured for 1,3,5-triazine and the carbonyl groups are predicted to be ketone-like rather than amide-like. The HB basicity predictions suggest that each carbonyl oxygen atom is associated with two HB acceptor sites. These are essentially equivalent for the C2 carbonyl group but the effects of the N7 HB donor on the C6 carbonyl are evident. When considering the crystal packing of theophylline, it is important to note that using one acceptor site of a carbonyl oxygen atom will weaken the HB basicity of the other site and Vmin has been used to examine this effect.29 Alternatively, one might focus on the HB donor/acceptor imbalance in the molecular structure of theophylline and the likelihood that the HB acceptors, although relatively weak, are of comparable strength. Neither of these factors is addressed specifically by Etter’s rules9 and one might conjecture that either factor could influence the tendency for a compound to crystallize in multiple polymorphic forms. CONCLUSION We have evaluated Vmin as a predictor of pKBHX across a range of HB acceptor types and used the models to make predictions when measured values are unavailable as is the case for acceptor types such as fluorine bonded to aromatic carbon. We have demonstrated that pKBHX predictions can provide insight into bioisosteric relationships and the curious case of the isomeric oxadiazoles prompts questions about the value of categorizing atoms and molecular surface as either polar or non-polar in practical lead optimization. We have shown how that through-space interactions can be harnessed to increase HB basicity independently of proton basicity and have suggested that certain arrangements of HB acceptors and donors may be ‘privileged’ in the sense that their interaction potential cannot be easily reproduced by clusters of water molecules. We


17


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 44

hope to have illustrated how consideration of HB basicity can help medicinal chemists gain a deeper understanding of the properties of the compounds that they seek to optimize. COMPUTATIONAL DETAILS Measured values of pKHXB and were taken from the literature and corrected statistically for the presence in the molecular structure of more than one equivalent acceptor. This data set is provided as supporting information which includes a link to the literature reference for each compound. Molecular structures were encoded as isomeric SMILES89,90 and a single conformation was generated for each using Omega91,92 and energy-minimized with Szybki93 using the MMFF94S94 force field. Quantum mechanical calculations were performed with Gaussian 0995 using Hartree-Fock96 (HF), B3LYP,97,98 M0699 and MP296,100,101 theoretical models with 6-31G** and 6-311+G** basis sets.102-105 Dimethylselenoxide and triethylarsine oxide were incompatible with the Omega/Szybki protocol and were energy-minimized at the HF/6-31G** level using Gaussian 09.95 Coordinates for starting points for MEP minimization were calculated using ADD_CENTRE that was created using the OEChem106 programming toolkit. This software is described in greater detail in the supplementary information and source code is also provided. All data analysis was performed using the JMP107 statistical analysis software and non-linear fitting was performed according to (3) in which V1, V2 and V3 are Vmin values associated with the HB acceptor atom of interest. pKBHX = A + log10( 10B×V1 + 10B×V2 + 10B×V3)

(3)


18

Page 19 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


In cases where fewer than three MEP minima were located, dummy minima were created by assigning a value of 1 Hartree/elemental charge to V3 and, if necessary, also to V2. Additional details are provided in the supplemental material. Predictions for pKBHX shown in Figure 2 were made using MP2/6-311+G** Vmin values and those in Figures 3 and 4 were made using HF/631G** Vmin values.


19


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 44

A

B

C

D

E

F

Figure 1. Plots of measured versus predicted pKBHX for nitrile nitrogen (A), heteroaromatic nitrogen (B), amines (C), carbonyl oxygen (D), singly-connected oxygen bonded to sulfur or selenium (E) and doubly-connected oxygen (F). The models depicted in this figure were fit using Vmin values calculated according to HF/6-31G** protocol (see computational details) and each model is specific to its atom type. The same scale has been used for all six plots to illustrate how the range in the data varies with atom type.


20

Page 21 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


B

A

Figure 2. Plot of measured versus predicted pKBHX for fluorine (A) and values of pKBHX predicted for structural prototypes (B) using Vmin values calculated using the MP2/6-311+G** protocol (see computational details section).

pKBHX predicted for 1 is for the compound

(measured67 value in parentheses) and the other predicted values of pKBHX are for individual fluorine atoms.


21


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 44

Figure 3. Predicted pKBHX values for individual hydrogen bond acceptors in oxadiazole and related bioisosteres. Methyl subsitituent was included to define alignment of bioisosteres and to lock tetrazoles and triazoles into desired tautomer. Measured33 pKBHX values for reference: furan (−0.40); 1,3,5-triazine (0.38; statistically corrected); isoxazole (0.81); oxazole (1.31); pyridine (1.86); N-methylimidazole (2.72).


22

Page 23 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 4. Predicted pKBHX values for individual hydrogen bond acceptors in some six-membered heteroaromatic rings. Measured33 pKBHX values for reference: 1,3,5-triazine (0.38; statistically corrected); pyrimidine (1.07; statistically corrected); pyridazine (1.65; statistically corrected); pyridine (1.86); quinoline (1.89); isoquinoline (1.94); 4-dimethylaminopyridine (2.80).


23


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 44

Figure 5. Measured logD7.4 and predicted pKBHX for phosphatidylinositol 3-kinase (PI3K) inhibitors.63 Arrows denote changes in logD7.4 associated with the relevant structural transformation. Measured33 pKBHX values for reference: 1,3,5-triazine (0.38; statistically corrected); pyrazine (0.92; statistically corrected); pyridazine (1.65; statistically corrected); pyridine (1.86); N,N-dimethylacetamide (2.44; no statistical correction for number of acceptor sites)


24

Page 25 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 6. Predicted pKBHX values for hydrogen bond acceptor sites of theophylline. Measured33 pKBHX values for reference: 1,3,5-triazine (0.38; statistically corrected); isoxazole (0.81); pyridine (1.86); N-methylimidazole (2.72); propanone (0.88; statistically corrected for number of HB acceptor sites); N,N-dimethylacetamide (2.44; no statistical correction for number of HB acceptor sites)


25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 44

Table 1. Hydrogen bond basicity models for different atom types QM model a

Atom Type

Nb

Ac

SE(A)

B/(e/Eh) e

SE(B)/(Eh/e)

RMSE g

SD h

f

d

Nitrile N

HF/6-31G**

16

−2.38

0.22

−36.61

2.47

0.117

0.460

Nitrile N

MP2/6-311+G**

16

−2.59

0.14

−43.99

1.79

0.072

0.460

Aromatic N

HF/6-31G**

58

−4.31

0.22

−54.79

1.97

0.163

0.622

Aromatic N

B3LYP/6-311+G**

58

−3.67

0.17

−56.09

1.84

0.151

0.622

Imine N

HF/6-31G**

22

−3.56

0.40

−47.16

3.38

0.189

0.604

Imine N

MP2/6-311+G**

22

−2.72

0.31

−47.96

3.06

0.177

0.604

Amine N

HF/6-31G**

60

−2.09

0.64

−33.20

5.29

0.333

0.428

Amine N

MP2/6-311+G**

60

−3.02

0.60

−46.78

5.69

0.293

0.428

Carbonyl O

HF/6-31G**

110

−4.05

0.17

−50.24

1.88

0.231

0.716

MP2/6-31G**

110

−3.58

0.13

−59.53

1.69

0.200

0.716

HF/6-31G**

8

−5.61

0.43

−72.25

4.19

0.182

1.312

HF/6-311G**

8

−5.65

0.34

−72.71

3.30

0.142

1.313

HF/6-31G**

12

−4.02

0.44

−52.80

4.18

0.255

1.027

O=[S,Se] j

M06/6-31G**

12

−3.40

0.33

−56.64

3.76

0.211

1.027

O=[N,P,As;X4]

HF/6-31G**

7

−4.34

0.94

−58.66

7.72

0.494

1.819

M06/6-311+G**

7

−4.98

0.90

−72.39

8.26

0.464

1.819

HF/6-31G**

47

−2.96

0.31

−39.86

3.51

0.257

0.507

HF/6-311+G**

47

−2.84

0.31

−38.71

3.53

0.271

0.507

Fluorine

HF/6-31G**

7

−1.11

0.28

−19.69

4.92

0.128

0.200

Fluorine

MP2/6-311+G**

7

−1.67

0.24

−30.94

4.77

0.106

0.200

Thiocarbonyl S

HF/6-31G**

5

−3.70

1.36

−59.50

18.9

0.282

0.500

Thiocarbonyl S

MP2/6-31G**

5

−2.79

0.46

−56.05

7.61

0.134

0.500

SX2 m

HF/6-31G**

12

−2.67

0.18

−42.18

3.32

0.068

0.272

HF/6-311+G**

12

−2.68

0.20

−44.39

3.82

0.073

0.272

HF/6-31G**

357

−3.05

0.09

−43.05

0.91

0.330

0.866

HF/6-311+G**

357

−3.18

0.09

−45.22

0.93

0.320

0.866

Carbonyl O O=[n,N;X3]

i

O=[n,N;X3] i O=[S,Se]

j

k

O=[N,P,As;X4] k

OX2 l OX2

SX2

l

m

All data n All data

n

a

Quantum mechanical model used for Vmin calculation

b

Number of observations

c

Intercept term in equation 1


26

Page 27 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


d

Standard error of intercept term in equation 1

e

Slope term in equation 1

f

Standard error of slope term in equation 1

g

Root mean square error

h

Standard deviation in pKBHX

i

Oxygen in nitro or heteroaromatic N-oxide

j

Oxygen doubly bonded to sulfur or selenium

k

Oxygen doubly-bonded to tetrahedral nitrogen, phosphorus or arsenic

l

Alcohols, ethers, furans and peroxides

m n

Thiols, thioethers and disulfides

Excluding 7 compounds in fluorine data subset

ASSOCIATED CONTENT Supporting Information. This consists of more detailed descriptions of the non-linear regression, the G09 set up, the ADD_CENTRE software, summary statistics for the full set of pKBHX models, the data used to build the models and source code for the ADD_CENTRE software. This material is available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION Corresponding Author *E-mail: [email protected] Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.


27


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 44

ACKNOWLEDGMENT We thank Fundacão de Amparo a Pesquisa do Estado de São Paulo (FAPESP) through the granting award number 2013/18009-4 for funding to carry out this work. IP, JR and GS thank FAPESP for scholarships. We are grateful to OpenEye Scientific Software for providing an academic license for software and the three reviewers of the manuscript for their constructive and insightful comments. ABBREVIATIONS B3LYP, Becke three-parameter exchange functional with Lee-Yang-Parr correlation functional; HF, Hartree-Fock; HB, hydrogen bond; M06, Minnesota 06 functional; MEP, molecular electrostatic potential; MP2, second order Møller-Plesset perturbation theory; RMSE, root mean square error; Vα(r), value of molecular electrostatic potential at distance r from hydrogen on extension of covalent bond to hydrogen; Vmin, value of molecular electrostatic potential at local minimum. REFERENCES 1. Latimer, W. M.; Rodebush, W. H. Polarity and ionization from the standpoint of the Lewis theory of valence. J. Am. Chem. Soc. 1920, 42, 1419-1433. 2. Pimental, G. C.; McClellan, A. L. The Hydrogen Bond; Freeman: San Francisco, 1960. 3. Abraham, M. H. Scales of solute hydrogen-bonding: their construction and application to physicochemical and biochemical processes. Chem. Soc. Rev. 1993, 22, 73-83. 4. Jeffrey, G. A. An Introduction to Hydrogen Bonding; Oxford University Press: Oxford, 1997.


28

Page 29 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


5. Williams, M. A.; Ladbury, J. E. Hydrogen bonds in protein-ligand complexes. Methods Princ. Med. Chem. 2003, 19, 137-161. 6. Arunan, E.; Desiraju, G. R.; Klein, R. A.; Sadlej, J.; Scheiner, S.; Alkorta, I.; Clary, D. C.; Crabtree, R. H.; Dannenberg, Joseph J.; Hobza, P.; Kjaergaard, H. G.; Legon, A. C.; Mennucci, B.; Nesbitt, D. J.; IUPAC Commission. Defining the hydrogen bond: an account (IUPAC Technical Report). Pure Appl. Chem. 2011, 83, 1619-1636. 7. Bissantz, C.; Kuhn, B.; Stahl, M. A medicinal chemist's guide to molecular interactions. J. Med. Chem. 2010, 53, 5061-5084. 8. Persch, E.; Dumele, O.; Diederich, F. Molecular recognition in chemical and biological systems. Angew. Chem. Int. Ed. 2015, 54, 3290-3327. 9. Etter, M. C. Encoding and decoding hydrogen-bond patterns of organic compounds Acc. Chem. Res. 1990, 23, 120-126. 10. Bernstein, J.; Davis, R. E.; Shimoni, L.; Chang, N.-L. Patterns in hydrogen bonding: functionality and graph set analysis in crystals. Angew. Chem., Int. Ed. 1995, 34, 15551573. 11. Karamertzanis, P. G.; Day, G. M.; Welch, G. W. A.; Kendrick, J.; Leusen, F. J. J.;Neumann, M. A.; Price, S. L. Modeling the interplay of inter- and intramolecular hydrogen bonding in conformational polymorphs. J. Chem. Phys. 2008, 128, 244708/1244708/17.


29


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 44

12. Watson, J. D.; Crick, F. H. C. Molecular structure of nucleic acids. A structure for deoxyribose nucleic acid. Nature (London) 1953, 171, 737-738. 13. Franklin, R. E.; Gosling, R. G. Molecular configuration in sodium thymonucleate. Nature (London) 1953, 171, 740-741. 14. Pauling, L.; Corey, R. B.; Branson, H. R. The structure of proteins: two hydrogen-bonded helical configurations of the polypeptide chain. Proc. Natl. Acad. Sci. U.S.A 1951, 37, 205-211. 15. Pauling, L.; Corey, R. B. Configurations of polypeptide chains with favored orientations around single bonds: two new pleated sheets. Proc. Natl. Acad. Sci. U.S.A. 1951, 37, 729740. 16. Baker, E. N.; Hubbard, R. E. Hydrogen bonding in globular proteins. Prog. Biophys. Mol. Biol. 1984, 44, 97-179. 17. McDonald, I. K.; Thornton, J. M. Satisfying hydrogen bonding potential in proteins. J. Mol. Biol. 1994, 238, 777-793. 18. Dill, K. A.; Ozkan, S. B.; Shell, M. S.; Weikl, T. R. The protein folding problem. Annu. Rev. Biophys. Biomol. Struct. 2008, 37, 289–316. 19. Fersht, A. R.; Shi, J. P.; Knill-Jones, J.; Lowe, D. M.; Wilkinson, A. J.; Blow, D. M.; Brick, P.; Carter, P.; Waye, M. M. Y.; Winter, G. Hydrogen bonding and biological specificity analyzed by protein engineering. Nature (London) 1985, 314, 235-238.


30

Page 31 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


20. Tanford, C. How protein chemists learned about the hydrophobic factor. Protein Sci. 1997, 6, 1358– 1366. 21. Southall, N. T.; Dill, K. A.; Haymet, A. D. J. A view of the hydrophobic effect. J. Phys. Chem. B 2002, 106, 521-533. 22. Leo, A.; Hansch, C.; Elkins, D. Partition coefficients and their uses. Chem. Rev. 1971, 71, 525-616. 23. Dearden, J. C.; Bresnen, G. M. The measurement of partition coefficients. Quant. Struct.Act. Relat. 1988, 7, 133-144. 24. Seiler, P. Interconversion of lipophilicities from hydrocarbon/water systems into the octanol/water system. Eur. J. Med. Chem. 1974, 9, 473-479. 25. Young, R. C.; Mitchell, R. C.; Brown, T. H.; Ganellin, C. R.; Griffiths; Jones, R. M.; Rana, K. K.; Saunders, D.; Smith, I. R.; Sore, N.; Wilks, T. J. Development of a new physicochemical model for brain penetration and its application to the design of centrally acting H2 receptor histamine antagonists. J. Med. Chem. 1988, 31, 656-671. 26. Leahy, D. E.; Morris J. J.; Taylor P. J.; Wait, A. R. Model solvent systems for QSAR. Part 2. Fragment values (f-values) for the critical quartet. J. Chem. Soc. Perkin. Trans. 2 1992, 723-731. 27. Abraham, M. H.; Chadha, H. S.; Whiting, G. S.; Mitchell, R. C. Hydrogen bonding. 32. An analysis of water-octanol and water-alkane partitioning and the ∆logP parameter of Seiler. J. Pharm. Sci. 1994, 83, 1085-1100.


31


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 32 of 44

28. Dearden, J. C.; Bresnen, G. M. Thermodynamics of water-octanol and water-cyclohexane partitioning of some aromatic compounds. Int. J. Mol. Sci. 2005, 6, 119-129 29. Toulmin, A.; Wood, J. M.; Kenny, P. W. Toward prediction of alkane/water partition coefficients. J. Med. Chem. 2008, 51, 3720-3730. 30. Shalaeva, M.; Caron, G.; Abramov, Y. A.; O'Connell, T. N.; Plummer, M. S.; Yalamanchi, G.; Farley, K. A.; Goetz, G. H.; Philippe, L.; Shapiro, M. J. Integrating intramolecular hydrogen bonding (IMHB) considerations in drug discovery using ∆logP as a tool. J. Med. Chem. 2013, 56, 4870-4879. 31. Taft, R. W.; Gurka, D.; Joris, L.; Schleyer, P. v. R.; Rakshys, J. W. Studies of hydrogenbonded complex formation with p-fluorophenol. V. Linear free energy relationships with OH reference acids J. Am. Chem. Soc. 1969, 91, 4801-4808. 32. Laurence, C.; Berthelot, M. Observations on the strength of hydrogen bonding. Perspect. Drug Discovery Des. 2000, 18, 39-60. 33. Laurence, C.; Brameld, K. A.; Graton, J.; Le Questel, J.-Y.; Renault, E. The pKBHX database: toward a better understanding of hydrogen-bond basicity for medicinal chemists. J. Med. Chem. 2009, 52, 4073-4086. 34. Abraham, M. H.; Duce, P. D.; Prior, D. V.; Barratt, D. G.; Morris, J. J.; Taylor, P. J. Hydrogen bonding. Part 9. Solute proton-donor and proton-acceptor scales for use in drug design. J. Chem. Soc., Perkin Trans. 2 1989, 1355-1375.


32

Page 33 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


35. Graton, J.; Berthelot, M.; Gal, J.-F.; Laurence, C.; Lebreton, J.; Le Questel, J.-Y.; Maria, P.-C.; Robins, R. The Nicotinic Pharmacophore: Thermodynamics of the hydrogenbonding complexation of nicotine, nornicotine, and models. J. Org. Chem. 2003, 68, 8208-8221. 36. Kenny, P. W. Hydrogen bonding, electrostatic potential and molecular design. J. Chem. Inf. Mod. 2009, 49, 1234-1244. 37. Murray, J. S.; Ranganathan, S.; Politzer, P. Correlations between the solvent hydrogen bond acceptor parameter β and the calculated molecular electrostatic potential. J. Org. Chem. 1991, 56, 3734-3739. 38. Kenny, P. W. Prediction of hydrogen bond basicity from computed molecular electrostatic properties: implications for comparative molecular field analysis. J. Chem. Soc., Perkin Trans. 2 1994, 199-202. 39. Hunter, C. A. Quantifying intermolecular interactions: guidelines for the molecular recognition toolbox. Angew. Chem., Int. Ed. 2004, 43, 5310-5324. 40. Klamt, A.; Reinisch, J.; Eckert, F.; Graton, J.; Le Questel, J.-Y. Interpretation of experimental hydrogen-bond enthalpies and entropies from COSMO polarisation charge densities. Phys. Chem. Chem. Phys. 2013, 15, 7147-7154. 41. Hohenstein, E. G.; Chill, S. T.; Sherrill, C. D. Assessment of the performance of the M05-2X and M06-2X exchange-correlation functionals for noncovalent interactions in biomolecules. J. Chem. Theory Comput. 2008, 4, 1996-2000.


33


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 34 of 44

42. Kenny, P. W.; Montanari, C. A.; Prokopczyk, I. M.; Sala, F. A.; Sartori, G. R. Automated molecule editing in molecular design. J. Comput.-Aided Mol. Des. 2013, 27, 655-664. 43. Andersson, C.; Hillgren, J. M.; Lindgren, C.; Qian, W.; Akfur, C.; Berg, L.; Ekstroem, F.; Linusson, A. Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase. J. Comput.-Aided Mol. Des. 2015, 29, 199-215. 44. Wassermann, A. M.; Wawer, M.; Bajorath, J. Activity landscape representations for structure-activity relationship analysis. J. Med. Chem. 2010, 53, 8209-8223. 45. Kenny, P. W.; Leitão, A.; Montanari, C. A. Ligand efficiency metrics considered harmful. J. Comp.-Aided Mol. Des. 2014, 28, 699-710. 46. Hann, M. M.; Leach, A. R.; Harper, G. Molecular complexity and its impact on the probability of finding leads for drug discovery. J. Chem. Inf. Comp. Sci. 2001, 41, 856864. 47. Zhou, H.-X.; Gilson, M. K. Theory of free energy and entropy in noncovalent binding. Chem. Rev. 2009, 109, 4092-4107. 48. Kangas, E.; Tidor, B. Electrostatic complementarity at ligand binding sites: application to Chorismate Mutase. J. Phys. Chem. 2001, 105, 880-888. 49. Ladbury, J. E.; Klebe, G.; Freire, E. Adding calorimetric data to decision making in lead discovery: a hot tip. Nat. Rev. Drug Discov. 2010, 9, 23-27.


34

Page 35 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


50. Davis, A. M.; Teague, S. J. Hydrogen bonding, hydrophobic interactions, and failure of the rigid receptor hypothesis. Angew. Chem. Int. Ed. 1999, 38, 736-749 51. Bethel, P. A.; Gerhardt, S.; Jones, E. V.; Kenny, P. W.; Karoutchi, G. I.; Morley, A. D.; Oldham, K.; Rankine, N.; Augustin, M.; Krapp, S.; Simader, H.; Steinbacher, S. Design of selective cathepsin inhibitors. Bioorg. Med. Chem. Lett. 2009, 19, 4622-4625. 52. Abraham, M. H.; Whiting, G. S.; Fuchs, R. J.; Chambers, E. J. Thermodynamics of solute transfer from water to hexadecane. J. Chem. Soc., Perkin Trans. 2 1990, 291-300. 53. Young, T.; Abel, R.; Kim, B.; Berne, B. J.; Friesner, R. A. Motifs for molecular recognition exploiting hydrophobic enclosure in protein-ligand binding Proc. Natl. Acad. Sci. 2007, 104, 808-813. 54. Beuming, T.; Che, Y.; Abel, R.; Kim, B.; Shanmugasundaram, V.; Sherman, W. Thermodynamic analysis of water molecules at the surface of proteins and applications to binding site prediction and characterization. Prot. Struct. Funct. Bioinf. 2012, 80, 871883 55. Morris, J. J.; Hughes, L. R.; Glen, A. T.; Taylor, P. J. Non-steroidal antiandrogens. Design of novel compounds based on an infrared study of the dominant conformation and hydrogen-bonding properties of a series of anilide antiandrogens. J. Med. Chem. 1991, 34, 447-455. 56. Atkinson, A. P.; Baguet, E.; Galland, N.; Le Questel, J.-Y.; Planchat, A.; Graton, J. Structural features and hydrogen-bond properties of galanthamine and codeine: an experimental and theoretical study. Chem. Eur. J. 2011, 17, 11637-11649.


35


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 36 of 44

57. Boström, J.; Hogner, A.; Llinas, A.; Wellner, E.; Plowright, A. T. Oxadiazoles in medicinal chemistry. J. Med. Chem. 2011, 55, 1817-1830. 58. Goldberg, K.; Groombridge, S.; Hudson, J.; Leach, A. G.; MacFaul, P. A.; Pickup, A.; Poultney, R.; Scott, J. S.; Svensson, P. H.; Sweeney, J. Oxadiazole isomers: all bioisosteres are not created equal. MedChemComm 2012, 3, 600-604. 59. Crawford, J. J.; Kenny, P. W.; Bowyer, J.; Cook, C. R.; Finlayson, J. E.; Heyes, C.; Highton, A. J.; Hudson, J. A.; Jestel, A.; Krapp, S.; Martin, S.; MacFaul, P. A.; McDermott, B. P.; McGuire, T. M.; Morley, A. D.; Morris, J. J.; Page, K. M.; Ribeiro, L. R.; Sawney, H.; Steinbacher, S.; Smith, C.; Dossetter, A. G. Pharmacokinetic benefits of 3,4-dimethoxy substitution of a phenyl ring and design of isosteres yielding orally available cathepsin K inhibitors. J. Med. Chem. 2012, 55, 8827-8837. 60. Plowright, A. T.; Barton, P.; Bennett, S.; Birch, A. M.; Birtles, S.; Buckett, L. K.; Butlin, R. J.; Davies, R. D. M.; Ertan, A.; Gutierrez, P. M.; Kemmitt, P. D.; Leach, A. G.; Svensson, P. H.; Turnbull, A. V.; Waring, M. J. Design and synthesis of a novel series of cyclohexyloxy-pyridyl derivatives as inhibitors of diacylglycerol acyl transferase 1. MedChemComm 2013, 4, 151-158. 61. Scott, J. S.; Berry, D. J.; Brown, H. S.; Buckett, L.; Clarke, D. S.; Goldberg, K.; Hudson, J. A.; Leach, A. G.; MacFaul, P. A.; Raubo, P.; Robb, G. Achieving improved permeability by hydrogen bond donor modulation in a series of MGAT2 inhibitors. MedChemComm 2013, 4, 1305-1311.


36

Page 37 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


62. Robb, G. R.; Boyd, Scott; Davies, C. D.; Dossetter, A. G.; Goldberg, F. W.; Kemmitt, P. D.; Scott, J. S.; Swales, J. G. Design of pyrazolo-pyrimidines as 11β-HSD1 inhibitors through optimisation of molecular electrostatic potential. MedChemComm 2015, 6, 926934. 63. Barlaam, B.; Cosulich, S.; Fitzek, M.; Green, S.; Harris, C. S.; Hudson, K.; Lambert-van der Brempt, C.; Ouvry, G; Page, K.; Ruston, L.; Ward, L.; Delouvrié, B. Design of selective PI3Kα inhibitors starting from a promiscuous pan kinase scaffold. Biorg. Med. Chem. Lett. 2015, 25, 2679-2685. 64. Spiess, A.-N.; Neumeyer, N. An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach. BMC Pharmacol. 2010, 10, 6. 65. Purser, S.; Moore, P. R.; Swallow, S.; Gouverneur, V. Fluorine in medicinal chemistry. Chem. Soc. Rev. 2008, 37, 320-330. 66. Hagmann, W. K. The many roles for fluorine in medicinal chemistry. J. Med. Chem. 2008, 51, 4359-4369. 67. Adcock, W.; Graton, J.; Laurence, C.; Lucon, M.; Trout, N. Three-center hydrogen bonding in the complexes of syn-2,4-difluoroadamantane with 4-fluorophenol and hydrogen fluoride. J. Phys. Org. Chem. 2005, 18, 227-234. 68. Dalvit, C.; Invernizzi, C.; Vulpetti, A. Fluorine as a hydrogen-bond acceptor: experimental evidence and computational calculations. Chem. Eur. J. 2014, 20, 1105811068.


37


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 38 of 44

69. Thornber, C. W. Isosterism and molecular modification in drug design. Chem. Soc. Rev.1979, 8, 563-580. 70. Patani, G. A.; LaVoie, E. J. Bioisosterism: a rational approach in drug design. Chem. Rev. 1996, 96, 3147-3176. 71. Sheridan, R. P. The most common chemical replacements in drug-like compounds. J. Chem. Inf. Comp. Sci. 2002, 42, 103-108. 72. Meanwell, N. A. Synopsis of some recent tactical application of bioisosteres in drug design. J. Med. Chem. 2011, 54, 2529-2591. 73. Birch, A. M.; Kenny, P. W.; Simpson, I.; Whittamore, P. R. O. Matched molecular pair analysis of activity and properties of glycogen phosphorylase inhibitors. Bioorg. Med. Chem. Lett. 2009, 19, 850-853. 74. Hansch, C.; Leo, A.; Hoekman, D. Exploring QSAR; American Chemical Society: Washington DC, 1995. 75. Graton, J.; Le Questel, J.-Y.; Maxwell, P.; Popelier, P. L. A. Hydrogen-bond accepting properties of new heteroaromatic rings chemical motifs: a theoretical study. J. Chem. Inf. Model. [Online early access]. DOI: 10.1021/acs.jcim.5b00574. Published Online: January 21, 2016. 76. Perrin D. D. Dissociation constants of organic bases in aqueous solution; Butterworths: London, 1965.


38

Page 39 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


77. Albert, A. Six-membered heteroaromatic rings containing nitrogen - correlation of structure and properties. Spec. Publ. 1955, 3, 124-138. 78. Edwards, J. O.; Pearson, R. G. The factors determining nucleophilic reactivities. J. Am. Chem. Soc. 1962, 84, 16-24. 79. Ohsawa, A.; Arai, H.; Ohnishi, H.; Igeta, H. 1,2,3-Triazine. J. Chem. Soc. Chem. Comm. 1981, 1174. 80. Albert, A. Naphthyridines. Ionization constants and spectra of four parent substances. J. Chem. Soc. 1960, 1790-1793. 81. Dennis, S.; Kortvelyesi, T.; Vajda, S. Computational mapping identifies the binding sites of organic solvents on proteins. Proc. Nat. Acad. Sci. 2002, 99, 4290-4295. 82. Blomberg, N.; Cosgrove, D. A.; Kenny, P. W.; Kolmodin, K. Design of compound libraries for fragment screening. J. Comp.-Aided Mol. Des. 2009, 23, 513-525. 83. Murray, C. W.; Rees, D. C. Opportunity knocks: organic chemistry for fragment-based drug discovery (FBDD). Angew. Chem. Int. Ed. 2016, 55, 488-492. 84. Hartshorn, M. J; Murray, C. W; Cleasby, A.; Frederickson, M.; Tickle, I. J; Jhoti, H. Fragment-based

lead

discovery

using

X-ray

crystallography.

J.

Med.

Chem. 2005, 48, 403-413. 85. Rowley, M.; Hallett, D. J.; Goodacre, S.; Moyes, C.; Crawforth, J.; Sparey, T. J.; Patel, S.; Marwood, R.;Patel, S.; Thomas, S.; Hitzel, L.; O’Connor, D.; Szeto, N.; Castro, J. L.; Hutson, P. H.; MacLeod, A. M. 3-(4-Fluoropiperidin-3-yl)-2-phenylindoles as high


39


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 40 of 44

affinity, selective, and orally bioavailable h5-HT2A receptor antagonists. J. Med. Chem. 2001, 44, 1603-1614. 86. Morissette, S. L.; Almarsson, O.; Peterson, M. L.; Remenar, J. F.; Read, M. J.; Lemmo, A. V.; Ellis, S.; Cima, M. J.; Gardner, C. R. High-throughput crystallization: polymorphs, salts, co-crystals and solvates of pharmaceutical solids. Adv. Drug Deliv. Rev. 2004, 56, 275-300. 87. Dunitz, J. D.; Bernstein, J. Disappearing polymorphs. Acc. Chem Res. 1995, 28, 193-200. 88. Bobrovs, R.; Seton, L.; Dempster, N. The reluctant polymorph: investigation into the effect of self-association on the solvent mediated phase transformation and nucleation of theophylline. CrystEngComm 2014, 16, 10581-10591. 89. Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comp. Sci. 1988, 28, 31-36. 90. Weininger, D.; Weininger, A.; Weininger, J. L. SMILES. 2. Algorithm for generation of unique SMILES notation. J. Chem. Inf. Comp. Sci. 1989, 29, 97-101. 91. OMEGA. OpenEye Scientific Software, Santa Fe, NM 87508. http://www.eyesopen.com/ omega 92. Hawkins, P.C.D.; Skillman, A.G.; Warren, G.L.; Ellingson, B.A.; Stahl, M.T. Conformer generation with OMEGA: algorithm and validation using high quality structures from the protein databank and the Cambridge structural database. J. Chem. Inf. Model. 2010, 50, 572-584.


40

Page 41 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


93. SZYBKI. OpenEye Scientific Software, Santa Fe, NM 87508. http://www.eyesopen.com/ szybki 94. Halgren; T. A. MMFF VI. MMFF94S option for energy minimization studies. J. Comp. Chem. 1999, 20, 720-729. 95. Gaussian 09, Revision A.1, Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G. A.; Nakatsuji, H.; Caricato, M.; Li, X.; Hratchian, H. P.; Izmaylov, A. F.; Bloino, J.; Zheng, G.; Sonnenberg, J. L.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Montgomery, Jr., J. A.; Peralta, J. E.; Ogliaro, F.; Bearpark, M.; Heyd, J. J.; Brothers, E.; Kudin, K. N.; Staroverov, V. N.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Rega, N.; Millam, J. M.; Klene, M.; Knox, J. E.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Zakrzewski, V. G.; Voth, G. A.; Salvador, P.; Dannenberg, J. J.; Dapprich, S.; Daniels, A. D.; Farkas, Ö.; Foresman, J. B.; Ortiz, J. V.; Cioslowski, J.; Fox, D. J. Gaussian, Inc., Wallingford CT, 2009. 96. Szabo, A.; Ostlund, N. S. Modern Quantum Chemistry. Introduction to Advanced Electronic Structure Theory; Dover: Mineola, 1996. 97. Becke, A. D. Density-functional thermochemistry. III. The role of exact exchange J. Chem. Phys. 1993, 98, 5648-5652.


41


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 42 of 44

98. Lee, C.; Yang, W.; Parr, R. G. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density Phys. Rev. B 1988, 3, 785-789. 99. Zhao, Y.; Truhlar, D. G. The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals. Theor. Chem. Account 2008, 120, 215-241. 100.

Møller, C.; Plesset, M. S. Note on the approximation treatment for many-electron

systems. Phys. Rev. 1934, 46, 618-622. 101.

Frisch, M. J.; Head-Gordon, M.; Pople, J. A. A direct MP2 gradient

method. Chem. Phys. Lett. 1990, 166, 275-280. 102.

Hehre, W. J.; Pople, J. A. Self-consistent molecular-orbital methods. IX.

Extended Gaussian-type basis for molecular-orbital studies of organic molecules. J. Chem. Phys. 1971, 54, 724-728. 103.

Frisch, M. J.; Pople, J. A.; Binkley, J. S. Self-consistent molecular orbital

methods.

25.

Supplementary

functions

for

Gaussian

basis

sets. J.

Chem.

Phys. 1984, 80, 3265-3269. 104.

Spitznagel, G. W.; Clark, T.; Chandrasekhar, J.; Schleyer, P. v. R. Stabilization of

methyl anions by first-row substituents. The superiority of diffuse function-augmented basis sets for anion calculations. J. Comput. Chem. 1982, 3, 363-371.


42

Page 43 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


105.

Krishnan, R.; Binkley, J. S.; Seeger, R.; Pople, J. A. Self-consistent molecular

orbital methods. XX. A basis set for correlated wave functions J. Chem. Phys. 1980, 72, 650-654. 106.

OEChem Toolkit. OpenEye Scientific Software, Santa Fe, NM 87508. http://

www.eyesopen.com/docs/toolkits/current/html/OEChem_TK-c++/index.html 107.

JMP version 12.0, SAS Institute, Cary, NC 27513. http://www.jmp.com


43


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 44 of 44

Table of contents graphic


44

Hydrogen Bond Basicity Prediction for Medicinal Chemistry Design

Recommend Documents