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Although log P is now recognized to be a key factor that determines the bioactivity of a molecule, the focus of medicinal chemists on hydrophobicity and log P started with the quantitative structure-activity relationships (QSAR) publications of Hansch and Fujita. Their original publication represents a dramatic change of focus to incorporate consideration of log P after a decade of work unsuccessfully attempting to use the Hammett equation to explain the structure-activity relationships of plant growth regulators. QSAR allows one to explore the quantitative relationship between log P and biological activity even when other factors also influence potency. In particular, Hansch's publications of thousands of QSAR equations demonstrate that a relationship of biological activity with log P is indeed a general phenomenon. Hansch's group also provided data and tools that enable others to explore the relationship between log P and the biological activity of compounds of interest.

Molecules that can tautomerize are a challenge to scientists because one must consider the possible tautomers in most tasks involving chemical structures: for example, searching databases, interpreting experimental property measurements, calculating properties, virtual screening, and analyzing structure-bioactivity relationships. The challenge in interpreting property measurements such as pK a values feeds into the general lack of extensive information not only of the relative tautomer stability in water but also the properties of the individual tautomers. This lack of information results in the lack of reliability of computational predictions of tautomer stability or properties. In spite of these problems, pK a calculations are reliable enough that they can be used to filter out high-energy tautomers from databases used for virtual screening. Continuous improvements in both pK a prediction software and theoretical calculations promise further improvements in solving the challenges of tautomers. The expected availability of high-resolution structures of many more tautomer-protein complexes will help guide the selection of the bioactive tautomer when the structure of the complex is not known., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

As molecular modellers we need to remember that the flexibility of a protein is necessary for it to function. Unfortunately, this flexibility is not readily apparent from the seductive molecular graphics rendering of cryocrystallographic results.

The novel selective D1 dopaminergic full agonists A-68930, A-77636 were discovered by the synthesis of molecules to probe the bioactive conformation of the partial agonist SKF-38393, by the use of this information to add D1 affinity and selectivity to a screening hit, and by traditional medicinal chemistry exploration of structure-activity relationships. The subsequent design of A-86929 and ABT-413 capitalized on these results, recently disclosed agonists, and traditional medicinal chemistry.

A consideration of equilibrium model-based equations suggests that tautomeric equilibria do not markedly affect observed potency if the tautomer bound represents at least 50% of the compound in solution. Tautomeric equilibria can enhance or attenuate the correlation of potency with Hammett sigma. Additionally, tautomeric equilibria can lead to a correlation of potency with sigma even in the absence of a correlation of binding with sigma.

A compound exhibits tautomerism if it can be represented by two structures that are related by an intramolecular movement of hydrogen from one atom to another. The different tautomers of a molecule usually have different molecular fingerprints, hydrophobicities and pKa's as well as different 3D shape and electrostatic properties; additionally, proteins frequently preferentially bind a tautomer that is present in low abundance in water. As a result, the proper treatment of molecules that can tautomerize, approximately 25% of a database, is a challenge for every aspect of computer-aided molecular design. Library design that focuses on molecular similarity or diversity might inadvertently include similar molecules that happen to be encoded as different tautomers. Physical property measurements might not establish the properties of individual tautomers with the result that algorithms based on these measurements may be less accurate for molecules that can tautomerize-this problem influences the accuracy of filtering for library design and also traditional QSAR. Any 2D or 3D QSAR analysis must involve the decision of if or how to adjust the observed Ki or IC50 for the tautomerization equilibria. QSARs and recursive partitioning methods also involve the decision as to which tautomer(s) to use to calculate the molecular descriptors. Docking virtual screening must involve the decision as to which tautomers to include in the docking and how to account for tautomerization in the scoring. All of these decisions are more difficult because there is no extensive database of measured tautomeric ratios in both water and non-aqueous solvents and there is no consensus as to the best computational method to calculate tautomeric ratios in different environments.

Between 2004 and 2008, the US National Institutes of Health Molecular Libraries and Imaging initiative pilot phase funded 10 high-throughput screening centers, resulting in the deposition of 691 assays into PubChem and the nomination of 64 chemical probes. We crowdsourced the Molecular Libraries and Imaging initiative output to 11 experts, who expressed medium or high levels of confidence in 48 of these 64 probes.

A wide variety of computational algorithms have been developed that strive to capture the chemical similarity between two compounds for use in virtual screening and lead discovery. One limitation of such approaches is that, while a returned similarity value reflects the perceived degree of relatedness between any two compounds, there is no direct correlation between this value and the expectation or confidence that any two molecules will in fact be equally active. A lack of a common framework for interpretation of similarity measures also confounds the reliable fusion of information from different algorithms. Here, we present a probabilistic framework for interpreting similarity measures that directly correlates the similarity value to a quantitative expectation that two molecules will in fact be equipotent. The approach is based on extensive benchmarking of 10 different similarity methods (MACCS keys, Daylight fingerprints, maximum common subgraphs, rapid overlay of chemical structures (ROCS) shape similarity, and six connectivity-based fingerprints) against a database of more than 150,000 compounds with activity data against 23 protein targets. Given this unified and probabilistic framework for interpreting chemical similarity, principles derived from decision theory can then be applied to combine the evidence from different similarity measures in such a way that both capitalizes on the strengths of the individual approaches and maintains a quantitative estimate of the likelihood that any two molecules will exhibit similar biological activity.

Phase-change storage is widely used in optical information technologies (DVD, CD-ROM and so on), and recently it has also been considered for non-volatile memory applications. This work reports advances in thermal data recording of phase-change materials. Specifically, we show erasable thermal phase-change recording at a storage density of 3.3 Tb inch(-2), which is three orders of magnitude denser than that currently achievable with commercial optical storage technologies. We demonstrate the concept of a thin-film nanoheater to realize ultra-small heat spots with dimensions of less than 50 nm. Finally, we show in a proof-of-concept demonstration that an individual thin-film heater can write, erase and read the phase of these storage materials at competitive speeds. This work provides important stepping stones for a very-high-density storage or memory technology based on phase-change materials.

Responding to a demonstrated need for scientists to forecast the permeability and bioavailability (F) properties of compounds before their purchase, synthesis, or advanced testing, we have developed a score that assigns the probability that a compound will have F > 10% in the rat. Neither the rule-of-five, log P, log D, nor the combination of the number of rotatable bonds and polar surface area successfully categorized compounds. Instead, different properties govern the bioavailability of compounds depending on their predominant charge at biological pH. The fraction of anions with >10% F falls from 85% if the polar surface area (PSA) is < or = 75 A(2), to 56% if 75 < PSA < 150 A(2), to 11% if PSA is > or = 150 A(2). On the other hand, whereas 55% of the neutral, zwitterionic, or cationic compounds that pass the rule-of-five have >10% F, only 17% of those that fail have > 10% F. This same categorization distinguishes compounds that are poorly permeable from those that are permeable in Caco-2 cells. Further validation is provided with human bioavailability values from the literature.

A detailed chemometric analysis of ligand binding to domain-3A of human serum albumin is described. NMR and fluorescence data on a set of 889 chemically diverse compounds were used to develop a group contribution model based on 74 chemical fragments that is in good agreement with the experimental data (R2 = 0.94, Q2 = 0.90). The structural descriptors used in this analysis comprise a convenient look-up table for quantitatively estimating the effect that a particular group will have on albumin binding. This information can be valuable for optimizing a particular series of compounds for drug development.

To design diverse combinatorial libraries or to select diverse compounds to augment a screening collection, computational chemists frequently reject compounds that are > or =0.85 similar to one already chosen for the combinatorial library or in the screening set. Using Daylight fingerprints, this report shows that for IC(50) values determined as a follow-up to 115 high-throughput screening assays, there is only a 30% chance that a compound that is > or = 0.85 (Tanimoto) similar to an active is itself active. Although this enrichment is greater than that found with random screening and docking to three-dimensional structures, this low fraction of actives within similar compounds occurs not only because of deficiencies in the Daylight fingerprints and Tanimoto similarity calculations but also because similar compounds do not necessarily interact with the target macromolecule in similar ways. The current study emphasizes the statistical or probabilistic nature of library design and that perfect results cannot be expected.

Molecular diversity needs to be considered when designing a screening set, whether a set of individual compounds or a combinatorial library. We have found that traditional substructure descriptors encode information relative to the biological properties of molecules. Of the methods tested, Ward's clustering is most effective. Combinatorial library design also requires consideration of the method of deconvolution and the combinatorial constraint. We have developed a genetic algorithm solution to this problem. Lastly, predicting affinity of molecules for a macromolecular target has been addressed with the use of a potential of mean force. There continues to be an opportunity for innovative computational approaches to solving problems in medicinal chemistry.

A new knowledge-based scoring function (PMF-score), implemented into the DOCK4 program, was used to screen a database of 3247 small molecules for binding to the FK506 binding protein (FKBP). The computational ranking of these compounds was compared to the binding affinities measured by NMR. It was demonstrated that small, weakly binding molecules have, on average, higher computational scores than nonbinders and are enriched in the upper ranks of the computational scoring lists. In addition, the results obtained with the PMF scoring function were superior (by 30-120% larger enrichment factors) to those obtained with the standard force field score of DOCK4. The reliable ranking of small, weakly binding molecules offers new ways of designing building blocks in combinatorial libraries as well as SAR by NMR libraries with the increased chance of identifying suitable lead compounds for drug design.

A fast, simplified potential-based approach is presented that estimates the protein-ligand binding affinity based on the given 3D structure of a protein-ligand complex. This general, knowledge-based approach exploits structural information of known protein-ligand complexes extracted from the Brookhaven Protein Data Bank and converts it into distance-dependent Helmholtz free interaction energies of protein-ligand atom pairs (potentials of mean force, PMF). The definition of an appropriate reference state and the introduction of a correction term accounting for the volume taken by the ligand were found to be crucial for deriving the relevant interaction potentials that treat solvation and entropic contributions implicitly. A significant correlation between experimental binding affinities and computed score was found for sets of diverse protein-ligand complexes and for sets of different ligands bound to the same target. For 77 protein-ligand complexes taken from the Brookhaven Protein Data Bank, the calculated score showed a standard deviation from observed binding affinities of 1.8 log Ki units and an R2 value of 0.61. The best results were obtained for the subset of 16 serine protease complexes with a standard deviation of 1.0 log Ki unit and an R2 value of 0.86. A set of 33 inhibitors modeled into a crystal structure of HIV-1 protease yielded a standard deviation of 0.8 log Ki units from measured inhibition constants and an R2 value of 0.74. In contrast to empirical scoring functions that show similar or sometimes better correlation with observed binding affinities, our method does not involve deriving specific parameters that fit the observed binding affinities of protein-ligand complexes of a given training set. We compared the performance of the PMF score, Böhm's score (LUDI), and the SMOG score for eight different test sets of protein-ligand complexes. It was found that for the majority of test sets the PMF score performs best. The strength of the new approach presented here lies in its generality as no knowledge about measured binding affinities is needed to derive atomic interaction potentials. The use of the new scoring function in docking studies is outlined.

Molecular diversity, combinatorial chemistry and automated synthesis are helping usher in a new age in medicinal chemistry. The tools and practices of computational chemistry and molecular modeling are rising to the challenges and opportunities presented by the current trends in drug discovery and design. Recent advances include a number of new and meaningful measures of molecular diversity and the use of genetic algorithms to help design diverse libraries.

An evaluation of the suitability of a number of structural descriptors and clustering methods for use in diversity selection is presented. The methods are compared by their success in simulated biological activity predictions. The results suggest that simple 2D structural descriptors are particularly effective and that hierarchical clustering methods are superior to the non-hierarchical methods traditionally used for diversity related tasks. Results are presented which suggest that the difference in the utility of the descriptors can be accounted for by the different extent to which each encodes information relevant to the forces of ligand-receptor binding.

The design of combinatorial mixture libraries should take account of a number of factors. This paper describes the application of a genetic algorithm to optimizing the diversity of libraries while minimizing the effort that will be needed to deconvolute the biological hits by mass-spectroscopic techniques. It differs from previous applications of genetic algorithms to combinatorial library design in that each chromosome encodes an entire library with the result that properties of the library are optimized. Our method is such that it is easily extensible to optimizing the distributions of any number of physical or other properties of the library. The method allows for the combinatorial constraint inherent in mixtures that every substituent at each diversity site must occur in combination with every substituent at every other site. We present results showing that the genetic algorithm can produce good library designs in timely manner.

Recent evidence indicating the therapeutic potential of cholinergic channel modulators for the treatment of central nervous system (CNS) disorders as well as the diversity of brain neuronal nicotine acetylcholine receptors (nAChRs) have suggested an opportunity to develop subtype-selective nAChR ligands for the treatment of specific CNS disorders with reduced side effect liabilities. We report a novel series of 3-pyridyl ether compounds which possess subnanomolar affinity for brain nAChRs and differentially activate subtypes of neuronal nAChRs. The synthesis and structure-activity relationships for the leading members of the series are described, including A-85380 (4a), which possesses ca.50 pM affinity for rat brain [(3)H]-(-)-cytisine binding sites and 163% efficacy compared to nicotine to stimulate ion flux at human alpha4beta2 nAChR subtype, and A-84543 (2a), which exhibits 84-fold selectivity to stimulate ion flux at human alpha4beta2 nAchR subtype compared to human ganglionic type nAChRs. Computational studies indicate that a reasonable superposition of a low energy conformer of 4A with (S)-nicotine and (-)-epibatidine can be achieved.

Although the statistical method of partial least squares (PLS) is widely used for the analysis of the relationship between molecular properties and biological potency, it is recognized that PLS detects only linear relationships. We tested two types of properties: simulated univariate data and electrostatic molecular field as a function of Hammett sigma constants. In both cases we compared relationships in which the function is linear, asymptotic, or rises to an optimum and then falls. We found that PLS analysis of the matrix of the distances between every pair of compounds detects all three types of relationships with the same quality of cross-validation. The successful application of the method requires that the distance matrices be constructed such that each contains only information about one property (for example, the electrostatic field around the functional group of interest). Carbo and Hodgkin similarities perform less well than distances.

Quantitative structure-activity relationships (QSAR) of 72 1-phenyltetrahydropyridazin-3(2H)-one (I) analogues are examined for the inhibitory potency (IC50) of 5-lipoxygenase in a broken cell. The potency is increased by lipophilic substituents at the 3'- and 4'-positions. Substituents with positive F value at the 4'-position also increase the potency, while substituents at the 3'-position with a positive R value decrease it. The potency also decreases as the size of the 2'- and/or 4'-substituents increases. Thioketone analogues are about 5 times more potent than the corresponding carbonyl analogues.

Quantitative structure-activity relationship (QSAR) and comparative molecular field analysis (CoMFA) have been applied to elucidate the mechanisms of genotoxicity (SOSIP) of nitrofuran derivatives on Escherichia coli PQ37. The following equation was developed: log SOSIP = -33.1qc2 + 1.00 log P - 1.50Isat - 1.19MR - 0.76I5,6 - 3.76; n = 40, r = 0.900, s = 0.475. The QSAR model clearly reveals three important factors, namely, electronic (qc2), hydrophobic (log P) and steric (MR, Isat, I5,6) contributing toward the genotoxic activity of this class of compounds. qc2, the charge on the c2 atom attached to the NO2 group, supports a furan ring opening mechanism in explaining the genotoxicity. The finding of the coefficient of 1 with log P conforms to our previous findings with several different classes of mutagens acting on different systems. CoMFA analysis clearly demonstrates its potential in unraveling the steric features of the molecules through contour maps. The CoMFA cross-validated model also supports the importance of the electronic factor. It could not reveal any hydrophobic influence because the interaction energies of the CH3 and H2O probes are collinear. QSAR (classical) and CoMFA, if used judiciously, may complement each other and enhance the applicability of SAR in drug design.

In the absence of a 3D structure of the target biomolecule, to propose the 3D requirements for a small molecule to exhibit a particular bioactivity, one must supply both a bioactive conformation and a superposition rule for every active compound. Our strategy identifies both simultaneously. We first generate and optimize all low-energy conformations by any suitable method. For each conformation we then use ALADDIN to calculate the location of points to be considered as part of the superposition. These points include atoms in the molecule and projections from the molecule to hydrogen-bond donors and acceptors or charged groups in the binding site. These positions and the relative energy of each conformation are the input to our new program DISCO. It uses a clique-detection method to find superpositions that contain at least one conformation of each molecule and user-specified numbers of point types and chirality. DISCO is fast; for example, it takes about 1 min CPU to propose pharmacophores from 21 conformations of seven molecules. We typically run DISCO several times to compare alternative pharmacophore maps. For D2 dopamine agonists DISCO shows that the newer 2-aminothiazoles fit the traditional pharmacophore. Using site points correctly identifies the bioactive enantiomers of indoles to compare with catechols whereas using only ligand points leads to selecting the inactive enantiomer for the pharmacophore map. In addition, DISCO reproduces pharmacophore maps of benzodiazepines in the literature and proposes subtle improvements. Our experience suggests that clique-detection methods will find many applications in computational chemistry and computer-assisted molecular design.

3-D database searching has many uses for a medicinal chemist. It can aid in the design of compounds to probe or to mimic the bioactive conformation of a natural ligand or to fit a hypothetical or experimental structure of a binding site. It also can identify existing molecules that meet these criteria--new uses for old molecules. If one has a database of active compounds, 3-D searching can validate or refute a pharmacophore hypothesis. The CoMFA method of 3DQSAR can be used to forecast the potency of the designed analogs. Also, the integration of CoMFA and 3-D searching concepts provides a framework for the design of a good series for CoMFA. In addition, CoMFA 3DQSAR coefficients provide a model of the binding site to facilitate the design of compounds that fit the pharmacophore and do not hit sterically unfavorable regions.

The applicability of a comparative molecular field analysis (CoMFA) method to reproduce and predict the pKa values of 28 clonidine-like imidazoline analogues and 16 2-substituted imidazoles has been investigated with the GRID force field. Molecular fields calculated with an H+ probe and AM1 partial atomic charges produced a correlation with a small standard deviation and a high correlation coefficient with cross validation. It was concluded that the CoMFA treatment of electrostatic effects is suitable for predicting pKa values and thus for the examination of the electronic effects in 3D quantitative structure-activity relationships.

A number of 5-substituted 3-[1-(2-benzoxazolyl)hydrazino]propanenitrile analogues have been studied as inhibitors of the rat pleural reverse passive Arthus reaction, and quantitative structure-activity relationships (QSAR) of these analogues have been examined. The QSAR equations indicate that hydrophilic substituents at the 5-position produce more potent compounds, while electron-releasing groups decrease activity. The results supplement QSAR data we previously obtained from the dermal reverse passive Arthus reaction.

Quantitative structure-activity relationships (QSAR) of the 1-phenyl-3-aminopyrazoline analogues as inhibitors of immune complex-induced inflammation have been studied. The correlation suggests that the overall size of the phenyl substituents are of importance, and bulky groups have negative effects on potency. The negative steric effects are gradually increased from ortho to meta to para positions. The negative steric effects were sometimes altered by the electronic effects of the substituents. Electron-releasing groups on the phenyl ring increased potency, while electron-withdrawing groups decreased it. Ortho substituents, however, have unaccounted for additional deleterious effects described here with an indicator variable. The octanol-water partition coefficient (log P) and dissociation constants (pKa) of the 1-(m-trifluoromethylphenyl)-3-aminopyrazoline analogue have been experimentally determined.

Experimentally determined octanol-water partition coefficient values of substituted phenyl beta-D-glucopyranosides are compared with the calculated values using the computer program CLOGP. The systematic deviation of the calculated values from the measured ones in this series suggests that caution is required when calculations are performed on classes of compounds where many of the partition coefficients have not been experimentally determined.

The structural requirements for inhibition of RBL-1 (rat basophilic leukemia) 5-lipoxygenase by 15-hydroxyeicosa-5,8,11,13-tetraenoic acid (15-HETE, 1) were studied by systematic chemical modifications of the molecule at the hydroxyl and carboxyl groups, the double bonds, and the carboxylate and omega side chains. The most potent inhibitors were analogues that contained a 5,8-cis,cis-diene system and acted as alternate substrates for the enzyme. However, several analogues in which the 5,8-diene had been reduced were also found to inhibit the enzyme. Inhibition of 5-lipoxygenase by 15-hydroxyeicosa-11,13-dienoic acid (15-HEDE) analogues was optimal in compounds that generally contained a free carboxyl group, a carboxylate side chain of nine carbons, an omega side chain of five or six carbons, a cis,trans- or trans,cis-11,13-diene or 11,13-diyne system, and a 15-hydroxyl group. Conversion of 15-HEDE to its 16-membered lactone reduced but did not eliminate 5-lipoxygenase inhibitory activity. In contrast, a 3- to 10-fold enhancement of activity occurred when 5,15-diHETE (58) or 5-HETE (56) were cyclized to their respective delta-lactones. Molecular modeling of 15-HEDE analogues, modified in the C11-C15 region, showed that inactive analogues protrude into regions in space not occupied by active analogues. These structural studies indicate that multiple regions are important for 5-lipoxygenase inhibition by both 15-HETE and 15-HEDE analogues and that no single region plays a predominant role in inhibition.

Subtypes of alpha adrenergic receptors were studied using selective adrenergic agonists. A-53693, A-54741, and related compounds were evaluated for their affinity for alpha receptor subtypes using radioligand binding techniques. Efficacy and potency were also evaluated using in vitro bioassays of alpha-1 receptors in rabbit aorta smooth muscle and alpha-2 receptors in the phenoxybenzamine-pretreated canine saphenous vein. Active and inactive compounds were then submitted for computer-assisted molecular modeling evaluation to ascertain the structural requirements for optimal potency and selectivity. Rigid catecholamines such as A-53693 display a high degree of selectivity for alpha-2 compared to alpha-1 receptors, probably because of the unique regions of space at the ligand binding site occupied by active compounds. Imidazolines such as A-54741 also interact with extremely high affinity and potency for alpha-2 receptors, and to a lesser extent at alpha-1 receptors. The spatial domains occupied by phenethylamines and imidazolines differ, each having unique regions of permissable space at alpha receptors. Compounds such as A-53693 and A-54741 are extremely useful probes of the molecular interactions of alpha agonistic compounds which will help in the design of even more selective drugs for alpha adrenergic receptors.

The octanol-water partition coefficients (log P) and the dissociation constants (pKa) of 3-[1-(2-benzoxazolyl)hydrazino]propanenitrile analogues have been determined, and quantitative structure--activity relationships (QSAR) of the analogues as inhibitors of immune complex-induced inflammation have been studied. A significant correlation is observed between log P and pi substituent constants, and between pKa and inductive-field (F) and resonance (R) constants. The QSAR equations indicate that smaller substituents both at the 5-position and/or at the side chain tend to make the compound more potent, while an electron-withdrawing group at the side chain tends to make the compound less potent. The predicted potencies of 14 of 18 additional monosubstituted and all six disubstituted analogues agree reasonably well with the observed activities.

MENTHOR is a database system for the storage and retrieval of three-dimensional coordinate and charge information on molecules as well as of traditional biological and physical properties. Our molecular graphics system retrieves from MENTHOR structural information in individual molecules and receptor map/macromolecular binding site hypotheses. Substructural searches of MENTHOR are used to find starting coordinates for molecular modeling and traditional database searches of MENTHOR identify compounds for which modeling is needed. It also forms the data to be searched with ALLADDIN, our substructure/geometric search program. MENTHOR expedites molecular modeling by organizing previous work and facilitating transmission of information between individuals. Examples from modeling of D-2 receptor agonists are shown.

ALADDIN is a computer program for the design or recognition of compounds that meet geometric, steric, and substructural criteria. ALADDIN searches a database of three-dimensional structures, marks atoms that meet substructural criteria, evaluates geometric criteria, and prepares a number of files that are input for molecular modification and coordinate generation as well as for molecular graphics. Properties calculated from the three-dimensional structure are described by either properties calculated from the molecule itself or from the molecule as compared to a reference molecule and associated surfaces. ALADDIN was used to design analogues to probe a bioactive conformation of a small molecule and a peptide, to test alternative superposition rules for receptor mapping of the D2 dopamine receptor, to recognize unexpected D2 dopamine agonist activity of existing compounds, and to design compounds to fit a binding site on a protein of known structure. We have found that series designed by ALADDIN show much more subtle variation in shape than do those designed by traditional methods and that compounds can be designed to be very close matches to the objective.

Regression analysis of the potency of inhibition of monoamine oxidase by 47 propynylamines revealed that there are three determinants of inhibitory potency: (1) the smallest substituent on the nitrogen must be methyl or hydrogen in order for any activity to be observed; (2) potency is parabolically related to pKa-the optimum pKa is 6.2; and (3) ortho-substituted benzylamine analogs are ten times more potent than predicted on the basis of pKa values. The optimum pKa cannot be explained by differences in fraction ionized but rather in terms of the multistep sequence whereby these compounds inhibit MAO. A very slight positive effect of hydrophobicity on potency was found. The potency of several analogs not included in the original analysis was predicted.

The synthesis and pharmacology of 2-(5,6-dihydroxy-1,2,3,4-tetrahydro-1-naphthyl)imidazoline (A-54741, 4), a very potent alpha-adrenergic agonist, are described. The change in biological activity resulting from variation of the carbocyclic ring size of 4 from four through seven members (2-5) is presented, as well as an explanation that accounts for this change in activity by considering the "exactness of fit" of these compounds to both the alpha 1- and alpha 2-adrenergic receptors. Compound 4 was found in vitro to be a full agonist with greater potency at the alpha 2 receptor (ED50 norepinephrine (NE)/ED50 4 = 188 +/- 22) than at the alpha 1 receptor (ED50 NE/ED50 4 = 13 +/- 2).

Because of the ambiguities of how to treat ionization in empirical equations which relate biological activity to partition coefficient by use of a (log P)2 term, a theoretical approach to the problem is proposed. Based on a simplified view of assays of potency following in vitro or continuous infusion administration of drugs, equations have been derived from a combination of mass law, equilibrium, and extrathermodynamic assumptions. In general form the equations which relate potency to partition coefficient (P) and degree of ionization (alpha) are the following. If the neutral form reacts with the receptor, log (1/C) =-log [1 + SIGMAM(DIPci) + sigman[aj/Pb(1-alpha4y]] + X. If the ionic form reacts with the receptor, log (1/C) =-log [1 + (1 - Alphan)/(alphan)[sigmam(diPci) + sigman[aj/Pb(1-alphaj)]]] + X. In this generalized model there are m nonaqueous compartments and n aqueous compartments of different pH. The parameters a, b, c, and d can be interpreted in terms of the model. The shape of the log (1/C) vs. log P curve may be asymptotic, linear, or composed of two portions of unequal slope which meet at an optimum or a bend. With the use of these equations it is possible to examine whether the ion or the neutral form is the active species and whether there is hydrophobic bonding to the receptor and/or an inert compartment. The models may be further extended to include terms other than log P and alpha.