复旦大学：《药物设计学》课程教学资源（虚拟实验室课外实践）Exercises for Drug Design Courses_Practice-II COMPARING LOG P CALCULATIONS BY THE GHOSE–CRIPPEN AND VILLAR METHODS

version date: 1 December 2006 EXERCISE lL. 4 COMPARING LOG P CALCULATIONS BY THE GHOSE-CRIPPEN AND VILLAR METHODS Laura giurato and Salvatore Guccione Department of Pharmaceutical Sciences, University of Catania, viale Andrea Doria 6, Ed 2 Citta Universitaria, /-95125 Catania, Italy Phone:+39 095 738-4020: Fax:+39 095 443604: E-mail: edufarm@unict it(LG); guccione @unict it(SG) Scientific computation is not an end itself. It must be implemented in the context of problems to be solved. Introduction Biological activity is not an invariant property of a geometrical arrangement of a subset of ligand atoms, but it is contingent on parameters of the whole molecule and on interactions external to the molecules themselves, i. e, with the receptor There are three major forces that are important in biochemical ligand binding: hydrophobic, dispersive, and electrostatic interactions. Molar refractivity is related to dispersive forces, and the molecular orbital charge distribution or the electrostatic potential at the van der Waals radius may be used for modeling the electrostatic interaction. However, the hydrophobic interaction, although probably the most important factor for biochemical interaction, is least understood. The term "hy drophobic interaction"refers to the force or the corresponding energy that operates between two or more nonpolar solutes in liquid water. Although the theoretical work on hydrophobic interactions led to a clear understanding of the molecular structure of aqueous solution, it has hardly begun to build a satisfactory theoretical description of the process that has a wide range of practical applica- bility. In such a situation, medicinal chemists try to model this interaction using a physicochemical sents nonregiospecific dispersive and electrostatic forces and the consequent entropic faco o ligand property which closely parallels hydrophobicity, namely, the partition coefficient of the molecules between water and a nonpolar solvent(usually n-octanol ). This property in fact repr For many years, the rational design of novel compounds with therapeutic activity was largely based on the use of free energy expressions and regression analysis techniques(Qsar)to relate structural and physicochemical properties of a set of compounds to their activities or affini ties at a given binding site. Among other properties, octanol/water partition coefficients led to suc cessful correlations in a variety of fields related to pharmacokinetics and drug design The importance of the partition coefficient as a parameter for drug design is due to its rele- vance to a number of steps in the pathway between the administration of a drug and its biologica endpoint such as the drug transport process( Subcellular Pharmacokinetic according to Balaz). Un derstanding the kinetics of ADMET (Administration, Distribution, Toxicity, Excretion and Toxicity) in terms of drug structure and properties is a key step for rational drug development. More-over, log P is the only readily accessible physicochemical property that can be related to the entropic change that accompanies the interaction between a drug and a receptor, which in most cases is the dehydration process that precedes it. Finally, it can also serve as a measure of the interaction be- tween the drug and the recept Log p is a property exceedingly difficult to measure in the real world. The issue becomes even more complicated if the measurements were done at a pH such that the log P had to be cor- rected for ionization. pKa values vary as much as log Ps, if not more: they are sometimes very tem- erature-sensitive. Log D values measured at the same ph but in different laboratories often vary widely. Another problem concerns compounds that can tautomerize or equilibrate between zwitte- rion and neutral form. Experimental values jump all over, especially for compounds with high log P(usually because of solubility issues, micelle formation, and a troublesome tendency of some

1 EXERCISE II.4 COMPARING LOG P CALCULATIONS BY THE GHOSE–CRIPPEN AND VILLAR METHODS Laura Giurato and Salvatore Guccione Department of Pharmaceutical Sciences, University of Catania, viale Andrea Doria 6, Ed. 2 Città Universitaria, I-95125 Catania, Italy Phone: +39 095 738-4020; Fax: +39 095 443604; E-mail: edufarm@unict.it (LG); guccione@unict.it (SG) Scientific computation is not an end itself. It must be implemented in the context of problems to be solved. Introduction Biological activity is not an invariant property of a geometrical arrangement of a subset of ligand atoms, but it is contingent on parameters of the whole molecule and on interactions external to the molecules themselves, i.e., with the receptor. There are three major forces that are important in biochemical ligand binding: hydrophobic, dispersive, and electrostatic interactions. Molar refractivity is related to dispersive forces, and the molecular orbital charge distribution or the electrostatic potential at the van der Waals radius may be used for modeling the electrostatic interaction. However, the hydrophobic interaction, although probably the most important factor for biochemical interaction, is least understood. The term “hy￾drophobic interaction” refers to the force or the corresponding energy that operates between two or more nonpolar solutes in liquid water. Although the theoretical work on hydrophobic interactions led to a clear understanding of the molecular structure of aqueous solution, it has hardly begun to build a satisfactory theoretical description of the process that has a wide range of practical applica￾bility. In such a situation, medicinal chemists try to model this interaction using a physicochemical property which closely parallels hydrophobicity, namely, the partition coefficient of the ligand molecules between water and a nonpolar solvent (usually n-octanol). This property in fact repre￾sents nonregiospecific dispersive and electrostatic forces and the consequent entropic factor. For many years, the rational design of novel compounds with therapeutic activity was largely based on the use of free energy expressions and regression analysis techniques (QSAR) to relate structural and physicochemical properties of a set of compounds to their activities or affini￾ties at a given binding site. Among other properties, octanol/water partition coefficients led to suc￾cessful correlations in a variety of fields related to pharmacokinetics and drug design. The importance of the partition coefficient as a parameter for drug design is due to its rele￾vance to a number of steps in the pathway between the administration of a drug and its biological endpoint such as the drug transport process (Subcellular Pharmacokinetic according to Balaz). Un￾derstanding the kinetics of ADMET (Administration, Distribution, Toxicity, Excretion and Toxicity) in terms of drug structure and properties is a key step for rational drug development. More-over, log P is the only readily accessible physicochemical property that can be related to the entropic change that accompanies the interaction between a drug and a receptor, which in most cases is the dehydration process that precedes it. Finally, it can also serve as a measure of the interaction be￾tween the drug and the receptor. Log P is a property exceedingly difficult to measure in the real world. The issue becomes even more complicated if the measurements were done at a pH such that the log P had to be cor￾rected for ionization. pKa values vary as much as log Ps, if not more: they are sometimes very tem￾perature-sensitive. Log D values measured at the same pH but in different laboratories often vary widely. Another problem concerns compounds that can tautomerize or equilibrate between zwitte￾rion and neutral form. Experimental values jump all over, especially for compounds with high log P (usually because of solubility issues, micelle formation, and a troublesome tendency of some version date: 1 December 2006

version date: 1 December 2006 to stick to the vessel walls; this is compounded by the accuracy of the assay procedure when a purported method to estimate log P claims accuracy to within 1 log unit, it must surely scare and, as any estimation method that claims to be better than experimental error, seems somehow surreal. Using the classic shake-flask method, log P measurements may be very time- consuming. Beginning with the"easy" solutes in the mid-1960s, a technician in Hansch's group usually measured a half-dozen per week, each over at least a five-fold concentration range and with at least three measurements deviating no more than 0.05 log units but the solutes of current inter- est are generally much more difficult to measure, especially those of very high or very low log P where the solvent ratios are 1000 to l or greater For extremely lipophilic solutes, it has been shown that measured log P values can asymp. totically approach a high log p value due to the global dissolving effect of octanol in the aqueous Thus, the accuracy of measurements of these excessively lipophilic solutes is not as much a problem as is the fact that the octanol/water model no longer reflects real-life situations Theoretical approaches for log P calculation are required to estimate its value for hypotheti cal structures, such as those that may be designed based on the QSars developed Several methods that allow the computation of partition coefficients have been proposed Each of these methods varies considerably in its rationale The partition coefficient of a solute in two solvents may be approximated as the ratio of its solubilities in the two solvents. The logarithm of partition coefficient, on the other hand, is directly lated to the change in free energy during the transfer of the solute from one solvent to the other One way of understanding the octanol/water partition coefficient is to correlate it with more fun- damental physicochemical properties, like molar volume, formal charge density, and polarizability With some modification, the atomic values of these fundamental properties can then be used to get the atomic contributions to partition coefficient. Although this approach is scientifically attractive there are several problems with it, particularly the conformational dependency of these fundamental properties for conformationally flexible molecules While a variety of different approaches have been proposed in the past for the computation of this property, most of them are based on two-dimensional molecular topology(vide infra). This feature renders them inadequate to describe the dependence of hydrophobicity on the three- dimensional arrangement of atoms. However, since flexible compounds can adopt different con- formations in solvents of different polarity, this dependence of the hydrophobic character on struc ture is a significant property. For instance, dependency of the conformation on the environment can be used in conjunction with other properties to identify the bioactive form for d i.e. the form in which the drug binds to the receptor The hydrophobicity of a molecular system depends on the nature of the groups exposed to interaction with the environment, and, therefore, it is dependent on the conformation of the system, whereas log P is not. Then, when three-dimensional structural properties of the ligands are included either in QSAR, COMF A analysis, or in mechanistic-based deduction of molecular determinants of receptor recognition, the use of log P is insufficient. A simple method to extend its usefulness would be to modify log P to relate to structure. However, it is possible to develop this relationship only for rigid analogs since in this case the interactions between the solute and the solvent are uniquely defined. Then, the relation thus established for rigid analogs can be used to characterize the hydrophobicity of particular conformations of flexible analogs. Such a conformationally de pendent hydrophobicity parameter would be a useful addition to other properties routinely used to characterize the bioactive structure of a ligand when no structural information about the receptor known. Without this information, the form of the ligand that is recognized by the receptor, i.e., its bioactive form is then deduced for each ligand based on the similarities in steric and electronic properties for analogs with high binding affinities and on the dissimilarities with all other analogs The lack of conformationally dependent hydrophobicity criteria prevents the use of this property mong those analyzed, in this crucial step toward the development of a pharmacophore

2 molecules to stick to the vessel walls; this is compounded by the accuracy of the assay procedure itself). So, when a purported method to estimate log P claims accuracy to within 1 log unit, it must surely scare and, as any estimation method that claims to be better than experimental error, seems somehow surreal. Using the classic shake-flask method, log P measurements may be very time￾consuming. Beginning with the “easy” solutes in the mid-1960s, a technician in Hansch’s group usually measured a half-dozen per week, each over at least a five-fold concentration range and with at least three measurements deviating no more than 0.05 log units. But the solutes of current inter￾est are generally much more difficult to measure, especially those of very high or very low log P where the solvent ratios are 1000 to 1 or greater. For extremely lipophilic solutes, it has been shown that measured log P values can asymp￾totically approach a high log P value due to the global dissolving effect of octanol in the aqueous phase. Thus, the accuracy of measurements of these excessively lipophilic solutes is not as much a problem as is the fact that the octanol/water model no longer reflects real-life situations. Theoretical approaches for log P calculation are required to estimate its value for hypotheti￾cal structures, such as those that may be designed based on the QSARs developed. Several methods that allow the computation of partition coefficients have been proposed. Each of these methods varies considerably in its rationale. The partition coefficient of a solute in two solvents may be approximated as the ratio of its solubilities in the two solvents. The logarithm of partition coefficient, on the other hand, is directly related to the change in free energy during the transfer of the solute from one solvent to the other. One way of understanding the octanol/water partition coefficient is to correlate it with more fun￾damental physicochemical properties, like molar volume, formal charge density, and polarizability. With some modification, the atomic values of these fundamental properties can then be used to get the atomic contributions to partition coefficient. Although this approach is scientifically attractive, there are several problems with it, particularly the conformational dependency of these fundamental properties for conformationally flexible molecules. While a variety of different approaches have been proposed in the past for the computation of this property, most of them are based on two-dimensional molecular topology (vide infra). This feature renders them inadequate to describe the dependence of hydrophobicity on the three￾dimensional arrangement of atoms. However, since flexible compounds can adopt different con￾formations in solvents of different polarity, this dependence of the hydrophobic character on struc￾ture is a significant property. For instance, dependency of the conformation on the environment can be used in conjunction with other properties to identify the bioactive form for a ligand, i.e., the form in which the drug binds to the receptor. The hydrophobicity of a molecular system depends on the nature of the groups exposed to interaction with the environment, and, therefore, it is dependent on the conformation of the system, whereas log P is not. Then, when three-dimensional structural properties of the ligands are included either in QSAR, COMFA analysis, or in mechanistic-based deduction of molecular determinants of receptor recognition, the use of log P is insufficient. A simple method to extend its usefulness would be to modify log P to relate to structure. However, it is possible to develop this relationship only for rigid analogs since in this case the interactions between the solute and the solvent are uniquely defined. Then, the relation thus established for rigid analogs can be used to characterize the hydrophobicity of particular conformations of flexible analogs. Such a conformationally de￾pendent hydrophobicity parameter would be a useful addition to other properties routinely used to characterize the bioactive structure of a ligand when no structural information about the receptor is known. Without this information, the form of the ligand that is recognized by the receptor, i.e., its bioactive form is then deduced for each ligand based on the similarities in steric and electronic properties for analogs with high binding affinities and on the dissimilarities with all other analogs. The lack of conformationally dependent hydrophobicity criteria prevents the use of this property, among those analyzed, in this crucial step toward the development of a pharmacophore. version date: 1 December 2006

version date: 1 December 2006 The characterization of a bioactive form is already one of the most challenging tasks in the development of pharmacophores for the computer-assisted design of novel drugs. Except for totally rigid analogs, the conformation in which the drug interacts with the receptor does not need to be its minimum energy structure. In most cases, candidate bioactive forms result from the analysis of steric and electronic similarities and dissimilarities among analogs with pharmacological profiles Hence, inclusion of information relative to influence of conformational dependence on key phys- icochemical properties of these compounds should facilitate the characterization of the bioactive Hopfinger and Bartell developed a solvent-dependent conformational analysis that allowed le computation of partition coefficients. The method was based on the precise evaluation of the first solvation shell far each solvent, making it computationally expensive A variation of the atomic contributions method was developed by Klopman and coworkers a quantum mechanical parameterization based on the computation of charge densities The rationale behind this latter approach is that the partition coefficient depends on the relative solubility of a substrate in a polar and nonpolar solvent. Since solubility in a polar envi- ronment can in turn be related to the electrostatic forces that involve charge densities, they post lated that the partition coefficient of a molecule should also be dependent on the charge densities More recently, Bodor et al. expanded the method originally developed by Klopman and co workers to include several other quantities such as the total molecular surface and its globularity, properties that are confomationally dependent, as well as the molecular weight, dipole moment,a olynomial of the sum of the atomic charges per atom, and an empirical parameter that assumed different values depending on the chemical family. Richards and coworkers have also developed a program called hYdRo that allows the computation of a conformation-dependent hydrophobic in- dex. The method consists of a series of fragment transfer free energies which are a function of the solvent-accessible surface area An atom-based method for the computation of a conformationally dependent hydrophobic index(p) is that by villa ar(see The alternate approach is to express the octanol/water partition coefficient in terms of the chemical structure of the ligand. Rekker et al. first gave some fragmental values for calculating the partition coefficient from the chemical structure of the molecules. Hansch and Leo followed the same direction and gave a thorough list of fragmental values and also a large number of correction factors to account for various intramolecular interactions. The implementation of these latter values in the CloGP program is the most reliable and widely used technique for the theoretical determi- nation of the partition coefficient. The calculation of the partition coefficient is performed by add- ing the contributions of molecular fragments. The major task in this method is the adequate definition of the fragments needed to generate a consistent parameterization. It is due to these cor- rection factors that regional contributions toward the partition coefficient are difficult to evaluate Alternative methods to determine this property are based on the addition of atomic contri butions, such as the methods developed by broto et al. and ghose et al. In this approach, tables of contributions for atoms in different topologic environments are used. The major advantage of this technique is that it lends itself to simple automation The solvatochromic approach pioneered by Kamlet, Taft, or that by Taylor, Leahy, Abra- ham points out that other partitioning pairs can add valuable information that log P octanol lacks a good program should provide some"distillation" of the hard-won wisdom contained in some hundreds of papers most researchers will not have read. The future challenge in improving log P(oct)calculation programs is to convey to the user as much understanding as possible of what determines the final parameter value, for instance, (i) predicting when a lipophilic environment en- intramolecular hydrogen bond to form when the solute is through a membrane or in a lipophilic pocket at the active site; (ii) the same sort of prediction when tautomerism is pos sible, e.g., encouraging the more lipophilic enol over the keto

3 The characterization of a bioactive form is already one of the most challenging tasks in the development of pharmacophores for the computer-assisted design of novel drugs. Except for totally rigid analogs, the conformation in which the drug interacts with the receptor does not need to be its minimum energy structure. In most cases, candidate bioactive forms result from the analysis of steric and electronic similarities and dissimilarities among analogs with pharmacological profiles. Hence, inclusion of information relative to influence of conformational dependence on key phys￾icochemical properties of these compounds should facilitate the characterization of the bioactive form. Hopfinger and Bartell developed a solvent-dependent conformational analysis that allowed the computation of partition coefficients. The method was based on the precise evaluation of the first solvation shell far each solvent, making it computationally expensive. A variation of the atomic contributions method was developed by Klopman and coworkers: a quantum mechanical parameterization based on the computation of charge densities. The rationale behind this latter approach is that the partition coefficient depends on the relative solubility of a substrate in a polar and nonpolar solvent. Since solubility in a polar envi￾ronment can in turn be related to the electrostatic forces that involve charge densities, they postu￾lated that the partition coefficient of a molecule should also be dependent on the charge densities. More recently, Bodor et al. expanded the method originally developed by Klopman and co￾workers to include several other quantities such as the total molecular surface and its globularity, properties that are confomationally dependent, as well as the molecular weight, dipole moment, a polynomial of the sum of the atomic charges per atom, and an empirical parameter that assumed different values depending on the chemical family. Richards and coworkers have also developed a program called HYDRO that allows the computation of a conformation-dependent hydrophobic in￾dex. The method consists of a series of fragment transfer free energies, which are a function of the solvent-accessible surface area. An atom-based method for the computation of a conformationally dependent hydrophobic index (p) is that by Villar (see below). The alternate approach is to express the octanol/water partition coefficient in terms of the chemical structure of the ligand. Rekker et al. first gave some fragmental values for calculating the partition coefficient from the chemical structure of the molecules. Hansch and Leo followed the same direction and gave a thorough list of fragmental values and also a large number of correction factors to account for various intramolecular interactions. The implementation of these latter values in the CLOGP program is the most reliable and widely used technique for the theoretical determi￾nation of the partition coefficient. The calculation of the partition coefficient is performed by add￾ing the contributions of molecular fragments. The major task in this method is the adequate definition of the fragments needed to generate a consistent parameterization. It is due to these cor￾rection factors that regional contributions toward the partition coefficient are difficult to evaluate. Alternative methods to determine this property are based on the addition of atomic contri￾butions, such as the methods developed by Broto et al. and Ghose et al. In this approach, tables of contributions for atoms in different topologic environments are used. The major advantage of this technique is that it lends itself to simple automation. The solvatochromic approach pioneered by Kamlet, Taft, or that by Taylor, Leahy, Abra￾ham points out that other partitioning pairs can add valuable information that log P octanol lacks. A good program should provide some “distillation” of the hard-won wisdom contained in some hundreds of papers most researchers will not have read. The future challenge in improving log P(oct) calculation programs is to convey to the user as much understanding as possible of what determines the final parameter value, for instance, (i) predicting when a lipophilic environment en￾courages an intramolecular hydrogen bond to form when the solute is passing through a membrane or in a lipophilic pocket at the active site; (ii) the same sort of prediction when tautomerism is pos￾sible, e.g., encouraging the more lipophilic enol over the keto. version date: 1 December 2006

version date: 1 December 2006 We are still a long way off being able to characterize molecules very well at all. There is no best "set of descriptors, although there are some that we know to be useful, and our feeling is the best "set will al ways be problem-dependent--and will probably feature properties from different What does this calculation tell me about how my promising molecule will behave when acted upon by the physical forces in a biological setting? " If several log P prediction programs give similar estimates(not absolute values, but trends that is some indication of the hydrophobicity of a molecule without having a perfect algorithm)of log P for a given compound, then one can have more confidence in these predictions. The most appropriate methods are based on fragmentation schemes, where explicit account is taken of the local environment of these fragments Different models are only useful in this context if they contain different information(differ- ent views)of the molecules concerned. So one would have to try and assess this point before ac- cepting that similar(or different) predictions for a property by different models actually have some meaning. The added value of a new method for estimating log P is difficult to estimate because it is always possible to find failures in comparison exercises. It is not annoying if the models have been well designed and if we keep in mind that a universal simulator does not exist and, consequently, ll models fail for specific structures. Although it is desirable to get a perfect algorithm, it should just be remembered that log P must give some indication of the hydrophobicity of a molecule With this preamble, suppose now that we want to estimate the log P value of a new chemi- cal. Combinations can be used. but the idea is to use models well designed and based on different methodologies. If these different models provide a rather similar log P value for our new chemical we can suppose that the simulation is acceptable. Conversely, high differences act as a warning message on the quality of the simulation Indeed, nowadays, by the combined use of models based on different methodologies,it possible to secure simulation results, while 20 years ago it was not possible. If this difference is important for QSPR models(e.g, log P, solubility), it is crucial in QSAR for toxicological end- points(e.g, mutagenicity, carcinogenicity) where the consequences of bad simulations can be dra matic for the launch of new molecules or for health In that case, the judicious use of different models for the same endpoint allows us to reduce the problems of false positives and false negatives. One question to ask is what are we estimating log P(or whatever) for? If we just have a few molecules, then one might as well just go and meas- ure it. While in the context of CombiChem, in silico screening we want quick estimates of log Pto help choose/eliminate structures. In this case, really accurate log P estimates are not necessary,we just need an approximation to test against the rule of five or something similar; we also need some indication of when the estimate is likely to be very wrong(back to ClogP again). Of course, in terms of science, it is interesting to be able to calculate accurately and precisely log P, but if one works in industry good science is a tool rather than an end in itself. Consequently, it is desirable that the number of new QSAr and QSPr models designed from very different methodologies will continue to increase in the future Furthermore, the risk with all these "property prediction"modules that are now more and more advertised is that in the end they produce meaningless numbers Isosterism, Bioisosterism, and Bioanalogy Confusion and fundamental problems with the continued use of both the terms"isosterism"and bioisosterism"remain, which calls for their revision and/or renaming. The word bioanalogue w proposed(Floersheim et al., 1992) to take the place of bioisostere and adopt the definition of Hansch, but expands its scope to accommodate groups or partial molecular structures as well as en- tire molecules. Thus, bioanalogs are molecules or groups which, in the context of a given biologi I parameter, elicit analogous responses. For this term, the structural connotations present in the previous term bioisostere are removed. Ascribing a bioanalogous relationship to different partial molecular structures or groups is associated with problems. Thus, for a given set of groups, bio-

4 We are still a long way off being able to characterize molecules very well at all. There is no “best” set of descriptors, although there are some that we know to be useful, and our feeling is the “best” set will always be problem-dependent—and will probably feature properties from different “sets”. “What does this calculation tell me about how my promising molecule will behave when acted upon by the physical forces in a biological setting?” If several log P prediction programs give similar estimates (not absolute values, but trends: that is some indication of the hydrophobicity of a molecule without having a perfect algorithm) of log P for a given compound, then one can have more confidence in these predictions. The most appropriate methods are based on fragmentation schemes, where explicit account is taken of the local environment of these fragments. Different models are only useful in this context if they contain different information (differ￾ent views) of the molecules concerned. So one would have to try and assess this point before ac￾cepting that similar (or different) predictions for a property by different models actually have some meaning. The added value of a new method for estimating log P is difficult to estimate because it is always possible to find failures in comparison exercises. It is not annoying if the models have been well designed and if we keep in mind that a universal simulator does not exist and, consequently, all models fail for specific structures. Although it is desirable to get a perfect algorithm, it should just be remembered that log P must give some indication of the hydrophobicity of a molecule. With this preamble, suppose now that we want to estimate the log P value of a new chemi￾cal. Combinations can be used, but the idea is to use models well designed and based on different methodologies. If these different models provide a rather similar log P value for our new chemical, we can suppose that the simulation is acceptable. Conversely, high differences act as a warning message on the quality of the simulation. Indeed, nowadays, by the combined use of models based on different methodologies, it is possible to secure simulation results, while 20 years ago it was not possible. If this difference is important for QSPR models (e.g., log P, solubility), it is crucial in QSAR for toxicological end￾points (e.g., mutagenicity, carcinogenicity) where the consequences of bad simulations can be dra￾matic for the launch of new molecules or for health. In that case, the judicious use of different models for the same endpoint allows us to reduce the problems of false positives and false negatives. One question to ask is what are we estimating log P (or whatever) for? If we just have a few molecules, then one might as well just go and meas￾ure it. While in the context of CombiChem, in silico screening we want quick estimates of log P to help choose/eliminate structures. In this case, really accurate log P estimates are not necessary, we just need an approximation to test against the rule of five or something similar; we also need some indication of when the estimate is likely to be very wrong (back to ClogP again). Of course, in terms of science, it is interesting to be able to calculate accurately and precisely log P, but if one works in industry good science is a tool rather than an end in itself. Consequently, it is desirable that the number of new QSAR and QSPR models designed from very different methodologies will continue to increase in the future. Furthermore, the risk with all these “property prediction” modules that are now more and more advertised is that in the end they produce meaningless numbers. Isosterism, Bioisosterism, and Bioanalogy Confusion and fundamental problems with the continued use of both the terms “isosterism” and “bioisosterism” remain, which calls for their revision and/or renaming. The word bioanalogue was proposed (Floersheim et al., 1992) to take the place of bioisostere and adopt the definition of Hansch, but expands its scope to accommodate groups or partial molecular structures as well as en￾tire molecules. Thus, bioanalogs are molecules or groups which, in the context of a given biologi￾cal parameter, elicit analogous responses. For this term, the structural connotations present in the previous term bioisostere are removed. Ascribing a bioanalogous relationship to different partial molecular structures or groups is associated with problems. Thus, for a given set of groups, bio- version date: 1 December 2006

version date: 1 December 2006 analogy may not necessarily be conserved over a wide range of molecules even with regard to a single biological parameter. Nevertheless, the concept of interchangeable groups which are poten- tially bioanalogous with regard to a single or many biological parameter(s) is indisputably very sefuL. Indeed, for the medicinal chemist, the greatest potential of the concept lies in systematic searching of a database to find group replacements which, starting from a lead molecule, could ield novel or useful active molecules The term"isostere"is so widespread that it can be maintained and continue to be used but only with regard to structure. It should, however, be redefined to be consistent with the current meaning of the adjective steric. Thus, isosteric literally means identical in size and shape but to be useful isosteric must be subjectively defined to mean similar in size and shape. with this classification, the terms"isosteric"and"non-isosteric bioanalogs"were proposed(Floersheim et al., 1992)to replace the terms"classical and "nonclassical bioisosteres'' Any consideration of isosterism has to take conformational parameters into account. Clearly, two conformations of the same molecule will not necessarily be isosteric; both, however, may be bioanalogous, if they can elicit similar biological activity Thus, it must be considered whether a chemical modification of a compound affects its confor- mational preferences, and through this mechanism, its biological activity. An additional difficulty may arise in attempting to relate molecules with analogous biological activity when racemic mix tures are used. As has been amply recognized by others, enantiomers interacting with chiral bio- molecules may differ greatly in their activities. Enantiomers are not isosteric, as they are by definition non-superimposable, but they may be bioanalogous. This is in agreement with pfeiffe rule, which refers to the generalization that the greater the biological activity of the racemate, the larger the difference in the activity of the enantiomers. This could be interpreted as the effect of a closer interaction between each of the enantiomers and the receptor, enhancing the stereodifferen- tiation. In other cases, however, Pfeiffers rule does not hold In conclusion, for constructing structure-activity relationships where biological activity is a consequence of a molecular recognition process, while certain empiricisms may be useful, struc- tural considerations grounded in first principles are of prime importance. Thus, isosterism, carefully defined on purely structural considerations, may point the way to bioanalogous molecules with a desirable pharmacological profile Floersheim P, Pombo-Villar E, Shapiro, G. Isosterism and Bioisosterism Case Studies with Muscarinic Agonists", Chimia 46, 323-334 (1992)and references cited therein Material and methods Molecular mechanics calculations and quantum chemical calculations play a multiple role in mod- ern-day computational chemistry. Molecular mechanics calculations on complex molecules may even be performed on personal computers and have spread widely throughout the chemical com- munity Quantum chemical calculations, even semiempirical molecular orbital calculations, but es- pecially ab initio molecular orbital calculations and density functional calculations, are much more time-demanding. Recently, with the availability of fast workstations and efficient graphics-based programs, these methods have begun to be widely applied Quantum chemical methods are also useful for furnishing information about the mechanism and product distributions of chemical reactions, either directly by calculations on transition states, or indirectly by modeling the steric and electronic demands of the reactants. Quantum chemical calculations are also able to supply information needed as input for other techniques, for example, atomic charges for QSAR analyses. Ab initio Hartree-Fock and correlated molecular orbital calcu lations, and density functional calculations are also able to provide accurate intra- and interno- lecular potentials. This kind of information is required both by molecular mechanics and by molecular dynamics techniques used to describe a wide variety of phenomena, ranging from inter actions between an enzyme and a drug to the physical properties of polymeric materials

5 analogy may not necessarily be conserved over a wide range of molecules even with regard to a single biological parameter. Nevertheless, the concept of interchangeable groups which are poten￾tially bioanalogous with regard to a single or many biological parameter(s) is indisputably very useful. Indeed, for the medicinal chemist, the greatest potential of the concept lies in systematic searching of a database to find group replacements which, starting from a lead molecule, could yield novel or useful active molecules. The term “isostere” is so widespread that it can be maintained and continue to be used but only with regard to structure. It should, however, be redefined to be consistent with the current meaning of the adjective steric. Thus, isosteric literally means identical in size and shape, but to be useful, isosteric must be subjectively defined to mean similar in size and shape. With this classification, the terms “isosteric” and “non-isosteric bioanalogs” were proposed (Floersheim et al., 1992) to replace the terms “classical” and “nonclassical bioisosteres”. Any consideration of isosterism has to take conformational parameters into account. Clearly, two conformations of the same molecule will not necessarily be isosteric; both, however, may be bioanalogous, if they can elicit similar biological activity. Thus, it must be considered whether a chemical modification of a compound affects its confor￾mational preferences, and through this mechanism, its biological activity. An additional difficulty may arise in attempting to relate molecules with analogous biological activity when racemic mix￾tures are used. As has been amply recognized by others, enantiomers interacting with chiral bio￾molecules may differ greatly in their activities. Enantiomers are not isosteric, as they are by definition non-superimposable, but they may be bioanalogous. This is in agreement with Pfeiffer’s rule, which refers to the generalization that the greater the biological activity of the racemate, the larger the difference in the activity of the enantiomers. This could be interpreted as the effect of a closer interaction between each of the enantiomers and the receptor, enhancing the stereodifferen￾tiation. In other cases, however, Pfeiffer’s rule does not hold. In conclusion, for constructing structure–activity relationships where biological activity is a consequence of a molecular recognition process, while certain empiricisms may be useful, struc￾tural considerations grounded in first principles are of prime importance. Thus, isosterism, carefully defined on purely structural considerations, may point the way to bioanalogous molecules with a desirable pharmacological profile. • Floersheim P., Pombo-Villar E., Shapiro, G. “Isosterism and Bioisosterism Case Studies with Muscarinic Agonists”, Chimia 46, 323–334 (1992) and references cited therein. Material and Methods Molecular mechanics calculations and quantum chemical calculations play a multiple role in mod￾ern-day computational chemistry. Molecular mechanics calculations on complex molecules may even be performed on personal computers and have spread widely throughout the chemical com￾munity. Quantum chemical calculations, even semiempirical molecular orbital calculations, but es￾pecially ab initio molecular orbital calculations and density functional calculations, are much more time-demanding. Recently, with the availability of fast workstations and efficient graphics-based programs, these methods have begun to be widely applied. Quantum chemical methods are also useful for furnishing information about the mechanisms and product distributions of chemical reactions, either directly by calculations on transition states, or indirectly by modeling the steric and electronic demands of the reactants. Quantum chemical calculations are also able to supply information needed as input for other techniques, for example, atomic charges for QSAR analyses. Ab initio Hartree–Fock and correlated molecular orbital calcu￾lations, and density functional calculations are also able to provide accurate intra- and intermo￾lecular potentials. This kind of information is required both by molecular mechanics and by molecular dynamics techniques used to describe a wide variety of phenomena, ranging from inter￾actions between an enzyme and a drug to the physical properties of polymeric materials. version date: 1 December 2006

version date: 1 December 2006 SPARTAN is a program that embraces molecular mechanics as well as ab initio and semiem- pirical molecular orbital and density functional methods. SPARTAN is intended to provide a con- venient environment to carry out individual molecular mechanics calculations, molecular orbital calculations, and density functional calculations on diverse molecular systems SPaRTaN Presently comprises seven independent program modules: a graphical user interface and ab initio, density functional, semi-empirical, mechanics, properties, and graphics modules SPARTAN'S architecture makes a clear separation between tasks and methods. Tasks indicate what is to be done(e.g, perform a geometry optimization or search conformational space), while methods dictate how the tasks are to be done(e. g use MMFF molecular mechanics or the Pm3 semiempirical method ) Both tasks and methods are specified in the graphical user interface SPARTAN'S graphical user interface provides a number of functions, among them: construction and editing of molecular structures, preparation of input designating the quantum chemical or mo- lecular mechanics calculation to be performed by the ab initio, density functional, semiempirical or mechanics modules; the preparation of input for Gaussian 94, preparation of input designating molecular properties to be calculated using the properties module SPARTANS ab initio module provides for calculation of the energy and wavefunction for a given nuclear configuration, of equilibrium or transition-state geometries and of normal-mode vi- brational frequencies. The module is presently limited to Hartree-Fock and MP2 correlated models, for both closed- and open-shell systems. SPARTAN'S semi-empirical module provides for the cal culation of heats of formation, equilibrium, and transition-state geometries and normal-mode vi- brational frequencies, as well as for searching of conformation space of both acyclic and cyclic molecules. The MNDO(Modified Neglect of Differential Overlap), AMl, and PM3 models are supported AMI and PM3 Semi-empirical Models Ab initio and semi-empirical models are based on the Hartree-Fock set u Both the para. digms"are strongly rigorous in regards to the solution of the Schrodinger but significant approximations are made to make the semi-empirical methods faster than their ab initio counter- parts. Semi-empirical methods are simplified versions of the Hartree-Fock theory using empirical methods that are derived from experimental data corrections in order to improve performance These methods are usually referred to through acronyms encoding some of the underlying theoreti cal assumptions. The most frequently used methods(MNDO, AMl, PM3)are all based on the Ne glect of Differential Diatomic Overlap(NDDO) integral approximation, while older methods use simpler integral schemes such as CNDO and INDO. all the three approaches belong to the class of Zero Differential Overlap(ZDO)methods, in which all two-electron integrals involving two-center charge distributions are neglected. A number of additional approximations are made to speed up calculations, and a number of parameterized corrections are made in order to correct for the ap- such that the calculated energies are expressed as heats of formations instead of total energia orme o proximate quantum mechanical model. How the parameterization is performed characterizes particular semi-empirical method. For MNDO, AMl, and PM3, the parameterization is performed Both AMI (Austin Model 1)and PM3 ( Parameterization Method 3 )are based on the basic nddo theory by Michael Dewar at the University of Texas, Austin. The substantial difference between the two methods is in the parameters used to partly replace the full ab initio implementation of the Hartree-Fock theory and in the less pronounced chemical sense of the PM3 model, built on a largely undirected mathematical optimization process, when compared to AMI. The stronger chemical character of the AMl method reflects on the better external performing of its parameters that is, the capability to yield useful results for situations not specifically included in the molecular basis set for parameterization(MBSP). Generally, AMI represents differences between compounds more reliably. PM3 works very well, for instance, when nitro-derivatives, extensively parameter ized in the MBSP, are calculated. PM3 performs sometimes better for geometries that are guessed <www.iupac.org/publications/cd/medicinalchemistry

6 SPARTAN is a program that embraces molecular mechanics as well as ab initio and semiem￾pirical molecular orbital and density functional methods. SPARTAN is intended to provide a con￾venient environment to carry out individual molecular mechanics calculations, molecular orbital calculations, and density functional calculations on diverse molecular systems. SPARTAN presently comprises seven independent program modules: a graphical user interface and ab initio, density functional, semi-empirical, mechanics, properties, and graphics modules. SPARTAN’s architecture makes a clear separation between tasks and methods. Tasks indicate what is to be done (e.g., perform a geometry optimization or search conformational space), while methods dictate how the tasks are to be done (e.g., use MMFF molecular mechanics or the PM3 semiempirical method). Both tasks and methods are specified in the graphical user interface. SPARTAN’s graphical user interface provides a number of functions, among them: construction and editing of molecular structures, preparation of input designating the quantum chemical or mo￾lecular mechanics calculation to be performed by the ab initio, density functional, semiempirical, or mechanics modules; the preparation of input for Gaussian 94, preparation of input designating molecular properties to be calculated using the properties module. SPARTAN’s ab initio module provides for calculation of the energy and wavefunction for a given nuclear configuration, of equilibrium or transition-state geometries and of normal-mode vi￾brational frequencies. The module is presently limited to Hartree–Fock and MP2 correlated models, for both closed- and open-shell systems. SPARTAN’s semi-empirical module provides for the cal￾culation of heats of formation, equilibrium, and transition-state geometries and normal-mode vi￾brational frequencies, as well as for searching of conformation space of both acyclic and cyclic molecules. The MNDO (Modified Neglect of Differential Overlap), AM1, and PM3 models are supported. AM1 and PM3 Semi-empirical Models Ab initio and semi-empirical models are based on the Hartree–Fock set of ideas. Both the “para￾digms” are strongly rigorous in regards to the solution of the Schrödinger equation, but significant approximations are made to make the semi-empirical methods faster than their ab initio counter￾parts. Semi-empirical methods are simplified versions of the Hartree–Fock theory using empirical methods that are derived from experimental data corrections in order to improve performance. These methods are usually referred to through acronyms encoding some of the underlying theoreti￾cal assumptions. The most frequently used methods (MNDO, AM1, PM3) are all based on the Ne￾glect of Differential Diatomic Overlap (NDDO) integral approximation, while older methods use simpler integral schemes such as CNDO and INDO. All the three approaches belong to the class of Zero Differential Overlap (ZDO) methods, in which all two-electron integrals involving two-center charge distributions are neglected. A number of additional approximations are made to speed up calculations, and a number of parameterized corrections are made in order to correct for the ap￾proximate quantum mechanical model. How the parameterization is performed characterizes the particular semi-empirical method. For MNDO, AM1, and PM3, the parameterization is performed such that the calculated energies are expressed as heats of formations instead of total energies. Both AM1 (Austin Model 1) and PM3 (Parameterization Method 3) are based on the basic NDDO theory by Michael Dewar at the University of Texas, Austin. The substantial difference between the two methods is in the parameters used to partly replace the full ab initio implementation of the Hartree–Fock theory and in the less pronounced chemical sense of the PM3 model, built on a largely undirected mathematical optimization process, when compared to AM1. The stronger chemical character of the AM1 method reflects on the better external performing of its parameters, that is, the capability to yield useful results for situations not specifically included in the molecular basis set for parameterization (MBSP). Generally, AM1 represents differences between compounds more reliably. PM3 works very well, for instance, when nitro-derivatives, extensively parameter￾ized in the MBSP, are calculated. PM3 performs sometimes better for geometries that are guessed version date: 1 December 2006

version date: 1 December 2006 with high precision. a big drawback in PM3 concerns the charges as a consequence of quite fiery parameter values Based on our experience, both models perform in the same way on data such as heats of formation, while problems were encountered with the PM3 model in minimum energy con- formations. In terms of the actual NDDO model, PM3 allows most of the parameter values to float resulting in substantially more parameters whether AMl has a different"view"in the application of the Gaussian functions considered as patches and introduced to adjust the core-electron/core electron repulsion function. The addition of Gaussian functions to some of the core repulsion func- tions(CRFs) for certain elements is a substantial difference between the two models. These func- tions have a position, a width, and an intensity, all of which can be taken as parameters. They were initially intended to adjust the SHAPE of the CRF so that it woulg. specific types of systems that re closely correspond to"re could not be handled in general parameterization without disrupting other chemically important items. To avoid failures common in all the models PM3 includes de novo two gaussian functions for each element, which strongly belittles thechemical reality Dewar, M.J.S., Healy, E F, Holder, A.J., Yuan, Y-C. "Comments on a comparison of AMI with the recently developed PM3 method",J. Comp. Chem. 11, 541-542(1990) Holder, A.J., Dennington, R.D., Jie, C " Addendum to SAMI results previously published 7 etrahedron50,627-638(1994) Stewart, J.J. P "MOPAC: A Semiempirical Molecular Orbital Program", J. Computer-Aided Mol.Des.4,1-105(1990) Gundertofte, K, Palm, J, Pettersson, I, Stamvik, A "A comparison of conformational energi calculated by molecular mechanics(MM2(85),Sybyl 5.1, Sybyl 5.21, and ChemXr-and semiempirical(AMI and PM3 )methods'",J Comp. Chem. 12, 200-208(1991) Among SPARTAN's properties module's functions, there is the calculation of log P Two log P models are available: Villar and Ghose-Crippen. The first is a model that only works on semi-empirical wavefunctions with no d-orbitals. The Villar method examines the over- lap matrix, searching for the type and number of lone pairs as well as the surface area of each atom It is parameterized for H, C,N, O, F, S, and CI Ghose-Crippen is the spartan default method of calculating log P. This method depend only on the connectivity of the molecule, and it is independent of the wavefunction (i.e, one will et the same results for semi-empirical, HF, and DFT methods but this depends on how the mole cule is drawn/connected). The Ghose-Crippen model is parameterized for 110 atom types, includ ing common bonds of H, C, N,O, S, and the halogens. Avoiding correction factors was obtained evaluating the hydrophobicity on an individual atom basis, accounting for the undeniable in- tramolecular interactions by employ ing a large number of atom types The Villar method is an alternative atom-based method for the computation of a conformation- ally dependent hydrophobic quantity(p). The parameters used in this method are the molecular sur- faces and atomic charges, both of which have some dependency on the conformation adopted by he system, as well as a set of adjustable parameters that only depend on the atomic number. These adjustable parameters were determined by linear regression using experimental values of the octa- nol/water partition coefficient The quantity p differs from the actual octanol/water partition coefficient, which is a macro- scopic property. Only if one conformer is accessible as in rigid compounds, are these two quantities equal. Even though the calculation of partition coefficients can be made using the hydrophobic in- dices described here, their computation would require a cumbersome statistical averaging for flexi- ble analogs. In general, this procedure is not recommended for the computation of the property when the methods proposed in the past can provide similar or better accuracies without the com- putational effort. One possible exception could be some types of isomery(see above, Isosterism, Bioisosterism, and Bioanalogy and the exercise below) which current methods for calculating

7 with high precision. A big drawback in PM3 concerns the charges as a consequence of quite fuzzy parameter values. Based on our experience, both models perform in the same way on data such as heats of formation, while problems were encountered with the PM3 model in minimum energy con￾formations. In terms of the actual NDDO model, PM3 allows most of the parameter values to float, resulting in substantially more parameters whether AM1 has a different “view” in the application of the Gaussian functions considered as patches and introduced to adjust the core-electron/core￾electron repulsion function. The addition of Gaussian functions to some of the core repulsion func￾tions (CRFs) for certain elements is a substantial difference between the two models. These func￾tions have a position, a width, and an intensity, all of which can be taken as parameters. They were initially intended to adjust the SHAPE of the CRF so that it would more closely correspond to “re￾ality”, whatever that is. In essence, they were used as patches for specific types of systems that could not be handled in general parameterization without disrupting other chemically important items. To avoid failures common in all the models, PM3 includes de novo two Gaussian functions for each element, which strongly belittles the “chemical” reality. • Dewar, M.J.S., Healy, E.F., Holder, A.J., Yuan, Y-C. “Comments on a comparison of AM1 with the recently developed PM3 method”, J. Comp. Chem. 11, 541–542 (1990). • Holder, A.J., Dennington, R.D., Jie, C. “Addendum to SAM1 results previously published”, Tetrahedron 50, 627–638 (1994). • Stewart, J.J.P. “MOPAC: A Semiempirical Molecular Orbital Program”, J. Computer-Aided Mol. Des. 4, 1–105 (1990). • Gundertofte, K., Palm, J., Pettersson, I., Stamvik, A. “A comparison of conformational energies calculated by molecular mechanics (MM2 (85), Sybyl 5.1, Sybyl 5.21, and ChemX) and semiempirical (AM1 and PM3) methods”, J. Comp. Chem. 12, 200–208 (1991). Among SPARTAN’s properties module’s functions, there is the calculation of log P. Two log P models are available: Villar and Ghose–Crippen. The first is a model that only works on semi-empirical wavefunctions with no d-orbitals. The Villar method examines the over￾lap matrix, searching for the type and number of lone pairs as well as the surface area of each atom. It is parameterized for H, C, N, O, F, S, and Cl. Ghose–Crippen is the SPARTAN default method of calculating log P. This method depends only on the connectivity of the molecule, and it is independent of the wavefunction (i.e., one will get the same results for semi-empirical, HF, and DFT methods but this depends on how the mole￾cule is drawn/connected). The Ghose–Crippen model is parameterized for 110 atom types, includ￾ing common bonds of H, C, N, O, S, and the halogens. Avoiding correction factors was obtained evaluating the hydrophobicity on an individual atom basis, accounting for the undeniable in￾tramolecular interactions by employing a large number of atom types. The Villar method is an alternative atom-based method for the computation of a conformation￾ally dependent hydrophobic quantity (p). The parameters used in this method are the molecular sur￾faces and atomic charges, both of which have some dependency on the conformation adopted by the system, as well as a set of adjustable parameters that only depend on the atomic number. These adjustable parameters were determined by linear regression using experimental values of the octa￾nol/water partition coefficient. The quantity p differs from the actual octanol/water partition coefficient, which is a macro￾scopic property. Only if one conformer is accessible as in rigid compounds, are these two quantities equal. Even though the calculation of partition coefficients can be made using the hydrophobic in￾dices described here, their computation would require a cumbersome statistical averaging for flexi￾ble analogs. In general, this procedure is not recommended for the computation of the property when the methods proposed in the past can provide similar or better accuracies without the com￾putational effort. One possible exception could be some types of isomery (see above, Isosterism, Bioisosterism, and Bioanalogy and the exercise below) which current methods for calculating version date: 1 December 2006

version date: 1 December 2006 partition coefficient do not distinguish. Nevertheless, the Villar hydrophobic indices are useful in themselves since they can provide important additional insights into drug-receptor interactions, leading to the characterization of a bioactive form Description(exercise Log P calculation for the compounds in Table I The first thing you have to do is draw the molecules. FIRST STI Using SParTaN software, build the molecules reported in Table I assigning the right(R or S)chi- rality. You can also retrieve the one of the enantiomers from the Smd(Spartan Molecular Data Make a copy of the enantiomeric entry and paste it into a list by Edit >Copy- File New Molecule --then Edit> Paste Go into the build mode [+ and holding down the ctrl key, double click on the Chiral Centre to INVERT from R to S(or S to R) Structure can also be retrieved from the CSD( Cambridge Struc- tural Database). An interface UNIX to the Cambridge Structural Database(CSD)* has been pro- vided in SPARTAN. This provides access to >225 K experimental X-ray crystal structures for organic and organometallic molecules together with their literature references In case you should download the ibuprofen from the CSD, assign the chirality to the two entries using the Fischer rule and retrieve the one that is correct (see the footnote in Table in Then, you have to obtain the equilibrium conformer for every drawn structure using "Equilib- rium Geometry at the"lowest"QM level, which is a Hartree-Fock calculation with the 3-21G ba- sis set. Start from the MMFF (Merck Molecular Force Field) conformer and the MMFF geometry Once the calculation has completed, you can view and/or print text and/or graphical output(Dis- play -Output). SPARTAN keeps track of what has already been done and will not repeat calcula- tions unnecessarily. While one(or more) job is running, input for another job can be constructed or output corresponding to yet another examined. These jobs may be derivative(i.e, based on infor- mation resulting from earlier jobs)or completely independent Lo LogP methods"can be selected with the LOGP=keyword: LOGP=VILLAR and GP=GHOSE, respectively Now you can submit the job as a Single Point Energy calculation using Semi-Empirical at AMI or PM3 level and starting from the initial geometry( this is from the hf calculation), one time writing LOGP=ViLLaR as keyword, another time LOGP=GhOSe

8 partition coefficient do not distinguish. Nevertheless, the Villar hydrophobic indices are useful in themselves since they can provide important additional insights into drug–receptor interactions, leading to the characterization of a bioactive form. Description (exercise) Log P calculation for the compounds in Table I. The first thing you have to do is draw the molecules. FIRST STEP: Using SPARTAN software, build the molecules reported in Table I assigning the right (R or S) chi￾rality. You can also retrieve the one of the enantiomers from the SMD (Spartan Molecular Data￾base). Make a copy of the enantiomeric entry and paste it into a list by: Edit > Copy -- File > New Molecule -- then Edit > Paste Go into the build mode [+] and holding down the CTRL key, double click on the Chiral Centre to INVERT from R to S (or S to R). Structure can also be retrieved from the CSD (Cambridge Struc￾tural Database). An interface UNIX to the Cambridge Structural Database (CSD)* has been pro￾vided in SPARTAN. This provides access to >225 K experimental X-ray crystal structures for organic and organometallic molecules together with their literature references. In case you should download the ibuprofen from the CSD, assign the chirality to the two entries using the Fischer rule and retrieve the one that is correct (see the footnote in Table II). Then, you have to obtain the equilibrium conformer for every drawn structure using “Equilib￾rium Geometry” at the “lowest” QM level, which is a Hartree–Fock calculation with the 3-21G ba￾sis set. Start from the MMFF (Merck Molecular Force Field) conformer and the MMFF geometry. Once the calculation has completed, you can view and/or print text and/or graphical output (Dis￾play → Output). SPARTAN keeps track of what has already been done and will not repeat calcula￾tions unnecessarily. While one (or more) job is running, input for another job can be constructed or output corresponding to yet another examined. These jobs may be derivative (i.e., based on infor￾mation resulting from earlier jobs) or completely independent. “LogP methods” can be selected with the LOGP=keyword: LOGP=VILLAR and LOGP=GHOSE, respectively. Now you can submit the job as a Single Point Energy calculation using Semi-Empirical at AM1 or PM3 level and starting from the initial geometry (this is from the HF calculation), one time writing LOGP=VILLAR as keyword, another time LOGP=GHOSE. version date: 1 December 2006

version date: 1 December 2006 Table ra Molecule Formula Limonene Trans-3-Methy cyclohexanecarboxylic acid HS Penicillamine OH Carvone H Ibuprofen OH N-iacetyl-penicillamine OH ( a)Asymmetric carbon is labeled by an asterisk

9 Table I(a) Molecule Formula Limonene * Trans-3-Methylcyclohexanecarboxylic acid * * OH O Penicillamine * HS OH NH2 O Carvone * O Homocamfin * Ibuprofen * O OH N-acetyl-penicillamine * HS OH HN O O (a)Asymmetric carbon is labeled by an asterisk. version date: 1 December 2006

version date: 1 December 2006 Table irod Molecule Ghose/Crippen Villar Limonene s 3.01 3.75 Limonene r 3.01 3.76 Trans-3-Methylcyclohexanecarboxylic acid S, S 2 1.10 Trans-3-Methylcyclohexanecarboxylic acid Rr 2 1.12 Penicillamine s Penicillamine r 0.38 0.30 Carvone s 2.41 2.33 Carvone Homocamfin s 2.84 2.33 Homocamfin r 2.84 2.34 N-acetyl-penicillamine S 0.45 0.30 N-acetyl-penicillamine R 045 0.27 profen s 3.72 3.75 Ibuprofen R(2) 3.66 (Using the Ghose-Crippen and the Villar methods in different environments or their implementation in different SPARTAN versions may lead to different absolute values but the trend should be the same(see text) (Every time you use crystallographic files carefully check their correctness(see the papers by Cozzini et al. and Spy- rakis et al. ) An ibuprofen entry with a wrong chirality is deposited in the CSD. *CSD needs to be licensed from the Cambridge Crystallographic Data Centre or one of its distributors As you can see, the Villar method is quite discriminating of the two enantiomers of the same mole- cule, while the Ghose-Crippen method gives the same value in both cases 1)Compare and comment on the results, observing how hydrophobicity changes according to the different structure and substituent chemical nature. Check for possible changes due to the different semi-empirical(AMI or PM3)used )Search the Internet for databases(see references) from which you can download experimental log P values. Whether it is possible(the available files are in a"chemical format )to cover the da- taset to a format suitable to be read in spartan orde novo,. sketch the structure in a suitable format for SPARTAN. If necessary, you can download the software BABEL (http://www.eyesopen.com/products/applications/babel.html).BesureallthelogPvaluesyouuse to build your dataset are homogeneous, which is determined by the same or quite close methods Homogeneity also applies to biological activity tests, which is one of the prerequisites to make con- sistent not"illusive""QSARs). Split your dataset, selecting rigid structures for calculations by the Ghose-Crippen method and conformationally flexible structures for the ones by the villar method Calculate the log Ps using both the Ghose-Crippen and the Villar methods. Prepare a diagram of experimental vS calculated values(Microsoft Excel can be used) and find for the best method to calculate your split dataset. Validate your "model", mixing rigid and flexible structures and calcu- late again using both the methods. Log Ps for flexible structures should be worst calculated by the Ghose-Crippen method. Extend and compare the analysis by the HiNT software(see the paper

10 Table II(1) Molecule Ghose/Crippen Villar Limonene S 3.01 3.75 Limonene R 3.01 3.76 Trans-3-Methylcyclohexanecarboxylic acid S,S 2 1.10 Trans-3-Methylcyclohexanecarboxylic acid R,R 2 1.12 Penicillamine S –0.38 –0.50 Penicillamine R –0.38 –0.30 Carvone S 2.41 2.33 Carvone R 2.41 2.38 Homocamfin S 2.84 2.33 Homocamfin R 2.84 2.34 N-acetyl-penicillamine S –0.45 0.30 N-acetyl-penicillamine R –0.45 0.27 Ibuprofen S(2) 3.72 3.75 Ibuprofen R (2) 3.66 3.75 (1)Using the Ghose–Crippen and the Villar methods in different environments or their implementation in different SPARTAN versions may lead to different absolute values but the trend should be the same (see text). (2)Every time you use crystallographic files carefully check their correctness (see the papers by Cozzini et al. and Spy￾rakis et al.). An ibuprofen entry with a wrong chirality is deposited in the CSD. *CSD needs to be licensed from the Cambridge Crystallographic Data Centre or one of its distributors. As you can see, the Villar method is quite discriminating of the two enantiomers of the same mole￾cule, while the Ghose–Crippen method gives the same value in both cases. 1) Compare and comment on the results, observing how hydrophobicity changes according to the different structure and substituent chemical nature. Check for possible changes due to the different “semi-empirical” (AM1 or PM3) used. 2) Search the Internet for databases (see references) from which you can download experimental log P values. Whether it is possible (the available files are in a “chemical format”) to cover the da￾taset to a format suitable to be read in SPARTAN or “de novo”, sketch the structure in a suitable format for SPARTAN. If necessary, you can download the software BABEL (http://www.eyesopen.com/products/applications/babel.html). Be sure all the log P values you use to build your dataset are homogeneous, which is determined by the same or quite close methods (homogeneity also applies to biological activity tests, which is one of the prerequisites to make con￾sistent not “illusive” QSARs). Split your dataset, selecting rigid structures for calculations by the Ghose–Crippen method and conformationally flexible structures for the ones by the Villar method. Calculate the log Ps using both the Ghose–Crippen and the Villar methods. Prepare a diagram of experimental vs. calculated values (Microsoft Excel can be used) and find for the best method to calculate your split dataset. Validate your “model”, mixing rigid and flexible structures and calcu￾late again using both the methods. Log Ps for flexible structures should be worst calculated by the Ghose–Crippen method. Extend and compare the analysis by the HINT software (see the paper version date: 1 December 2006