Novel methods for the prediction of logP,pK(a), and logD

Novel methods for predicting logP, pK(a), and logD values have been developed using data sets (592 molecules for logP and 1029 for pK(a)) containing a wide range of molecular structures. An equation with three molecular properties (polarizability and partial atomic charges on nitrogen and oxygen) correlates highly with logP (r2 = 0.89). The pK(a)s are estimated for both acids and bases using a novel tree structured fingerprint describing the ionizing centers. The new models have been compared with existing models and also experimental measurements on test sets of common organic compounds and pharmaceutical molecules.     

Prediction of cyclohexane-water distribution coefficients with COSMO-RS on the SAMPL5 data set

The Conductor-Like-Screening-Model for Real Solvents (COSMO-RS) method has been used for the blind prediction of cyclohexane-water distribution coefficients logD within the SAMPL challenge. The partition coefficient logP of the neutral species was calculated first and then corrected for dissociation or protonation, as appropriate for acidic or basic solutes, to obtain the cyclohexane-water logD. Using the latest version of the COSMOtherm implementation, this approach in combination with a rigorous conformational sampling yielded a predictive accuracy of 2.11 log units (RMSD) for the 53 compounds of the blind prediction dataset. By that it was the most accurate of all contest submissions and it also achieved the best rank order. The RMSD mainly arises from a group of outliers in the negative logD range, which at least partly may arise from dimerization or other experimental problems coming up for very polar molecules in very non-polar solvents.     

An empirical relationship between distribution coefficients of phenols by polyurethane foams and their octanol-water distribution constants and pK(a) values

Sorption of phenol, 3-cresol, 2-, 3-, 4-nitrophenols, 2,4-, 2,6-dinitrophenol, 2,4,6-trinitrophenol, and 1-naphthol by polyether- and polyester-type polyurethane foams (PUF) was investigated. The effects of sorption time, pH, phenol concentration and the structure of tested phenols and PUF were studied. The mechanism of sorption of tested compounds on foams is discussed. It is shown that the hydrophobicity (logP, octanol-water distribution constant) and pK(a) values of the compounds play an important role in the sorption process. A regression equation connecting distribution coefficient of phenols by PUF with their hydrophobicity parameter and pK(a) values were derived. Good correlation between logD and values logP and pK(a) was observed.     

Comprehensive evaluation of lipophilicity of biogenic amines and related compounds using different chemically bonded phases and various descriptors

The retention behavior for a series of biogenic amines and related sympathomimetic drugs has been investigated in reversed-phase thin-layer chromatography using RP-2, RP-8, RP-18W, and Diol stationary phase and mixtures of phosphate buffer (pH = 7.10) and methanol in different proportions as mobile phases. Several methodologies like arithmetic mean of experimental retention values, extrapolation to zero methanol concentration procedure and principal component analysis were applied to retention data values (R(M)) in order to determine relevant parameters (mean of R(M) - mR(M), R(M0), and scores corresponding to the first principal component - PC1/R(M) respectively) encoding information on the lipophilic behavior of compounds. High similarities in lipophilicity behavior of investigated amines were highlighted by mR(M) and PC1/R(M) lipophilicity indices for all of the studied stationary phases. The experimental results were compared with some computed lipophilicity parameters expressed as distribution coefficients at working pH (logD), partition coefficients (logP(N), logP(I), and diff(logP(N-I))) concerning both neutral and fully protonated species and difference between both species, and also with various lipophilicity values (logP) generated by different commonly used software. Significant correlations were observed between the experimental lipophilicity indices mR(M) respectively PC1/R(M) and diff(logP(N-I) ) values in all cases.     

Coulomb and overlap self-similarities: a comparative selectivity analysis of structure-function relationships for auxin-like molecules

Auxins are defined mainly by a set of physiological actions, but the structure-effect relationship still is based on chemical intuition. Currently a well-defined auxin molecular structure is not available. The existence of different auxin binding proteins and mechanisms of auxin action, the wide diversity of the auxin molecules, and the pleiotropic effects of auxin imply a completely different mechanism as described for the animal hormone concept. Here, we present a computational approach dealing with semiempirical optimizations of the auxin molecules themselves, which represent a number of about 250 different chemical structures. Our approach uses molecular quantum similarity measures and additional quantum variables for the analysis of auxin-like molecules. The finding of similarities in molecules by focusing basically on their electron structure results in new insights in the relationship of the different auxin groups. Additional statistical analysis allows the identification of relationships between similarity groups and their biological activity, respectively. It is postulated that the auxin-like molecular recognition depends more on specific molecular assembling states than on a specific ring system or side chain.     

2D-QSAR Studies on Pyrazole Compounds Containing Pyrimidine Amino

The quantitative structure-activity relationship (QSAR) of 16 pyrazole compounds was studied by ab initio method at the HF/3-21G level using Guassian03 soft. The optimized structures together with some characteristic and electric parameters of the title compounds were obtained; some stereo-parameters were calculated by HyperChem software. Stepwise multiple regression method was adopted to establish bi-and tri-parametric models between biological activity and some parameters. The lager ΔE and logP,the higher biological activity; and the biological activity would be promoted with the smaller μ,QN and QPYRA. It provided a theory direction to synthesize some compounds with high activity.     

Octanol/water partitioning simulation by reversed-phase high performance liquid chromatography for structurally diverse acidic drugs: Effect of n-octanol as mobile phase additive

The role of n-octanol as mobile phase additive for the lipophilicity assessment of 45 structurally diverse acidic drugs both at neutral (pH 2.5) and ionized form (pH 7.4) was investigated. Extrapolated retention factors logk(w) were determined on a BDS C18 column using methanol as organic modifier and different amounts of n-octanol as mobile phase additive. For more polar compounds, the effect of n-octanol in retention was found to decrease as their lipophilicity increased. In the case of carboxylic acids and oxicams, the differentiation in retention, in presence and absence of n-octanol, could be further attributed to the attenuation of polar interactions, concerning mainly hydrogen bonding. At pH 2.5, the use of n-octanol saturated buffer, without further addition of n-octanol in the mobile phase, led to 1:1 correlation with logP. At physiological pH, 1:1 correlation was obtained between logD(7.4) and logk(w)(oct) indices upon addition of 0.25% n-octanol, in the case of weak acids. For strongly ionized compounds, a good correlation was also established under the same conditions. The corresponding equation, however, possessed a large negative intercept and a slope lower than unity.     

logD7.4 modeling using Bayesian Regularized Neural Networks. Assessment and correction of the errors of prediction

Bayesian Regularized Neural Networks (BRNNs) employing Automatic Relevance Determination (ARD) are used to construct a predictive model for the distribution coefficient logD7.4 from an in-house data set of 5000 compounds with experimental endpoints. A method for assessing the accuracy of prediction is established based upon a query compound's distance to the training set. logD7.4 predictions are also dynamically corrected with an associated library of compounds of continuously updated, experimentally measured logD7.4 values. A comparison of local models and associated libraries comprising separate ionization class subsets of compounds to compounds of a homogeneous ionization class reveals in this case that local models and libraries have no advantage over global models and libraries.     

Application of belief theory to similarity data fusion for use in analog searching and lead hopping

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.     

Neighborhood behavior of in silico structural spaces with respect to in vitro activity spaces-a novel understanding of the molecular similarity principle in the context of multiple receptor binding profiles

As a consequence of recent advances in the field of High Throughput Screening, the systematic testing ("in vitro profiling") of compounds against a panel of targets covering different therapeutic areas is nowadays used to generate relevant information with respect to the in vivo behavior of drug candidates. However, the development of chemoinformatics tools required for the exploitation of such data is yet in an incipient phase. In this paper, a formalism for the analysis of activity profile vectors (describing the experimental responses of compounds in each of the considered activity tests) is introduced and applied at the study of Neighborhood Behavior (NB; the hypothesis that structurally similar compounds display similar biological properties) of molecular similarity metrics. The experimental activity profiles define an Activity Space in which more than 500 drugs and reference compounds are positioned, their coordinates being inhibitory propensities in the included tests and unambiguously characterizing a molecule in terms of its receptor binding properties. While previous studies of Neighborhood Behavior had to rely on a loose classification of compounds in terms of the therapeutic areas they were designed for, here the NB of a calculated "in silico" similarity metric has been redefined as a relationships between intermolecular dissimilarity scores in the "structural" and "activity" spaces, respectively, and expressed in terms of two quantitative criteria: "consistency" (the propensity of the metric to selectively rank activity-related compound pairs among the structurally most similar pairs) and "completeness" (monitoring the retrieval rate of activity-related compound pairs among the best ranked pairs of structural neighbors). These criteria were used to calibrate and validate a similarity metric based on Fuzzy Bipolar Pharmacophore Fingerprints.     

High-throughput logP measurement using parallel liquid chromatography/ultraviolet/mass spectrometry and sample-pooling

A novel approach to high-throughput logP measurement based on liquid chromatography/ultraviolet/mass spectrometry (LC/UV/MS) is proposed. The logP value is determined by correlation with the logk value, where k is the capacity factor k = (t(r)-t(0))/t(0), with the logP value using a defined set of standards. Since the analyte retention time (t(r)) is determined from the appropriate extracted ion chromatogram (EIC), there are no interferences from impurities and this allows the pooling of multiple compounds into one injection. To ensure the accuracy and instrument robustness in a routine high-throughput environment, a simple and MS-friendly mobile phase consisting of 20 mM ammonium carbonate (pH 8.0) for basic compounds or 20 mM ammonium formate (pH 1.0) for acidic compounds, both in combination with methanol at a ratio of 45:55, is used. This approach has been successfully used on single as well as parallel multi-channel LC/UV/MS systems to screen small to large sets of lead compounds and their analogs. A high-throughput capability to analyze over 1000 compounds per day has been achieved.     

An alignment‐free methodology for modelling field‐based 3D‐structure activity relationships using inductive logic programming

Traditional 3D‐quantitative structure–activity relationship (QSAR)/structure–activity relationship (SAR) methodologies are sensitive to the quality of an alignment step which is required to make molecular structures comparable. Even though many methods have been proposed to solve this problem, they often result in a loss of model interpretability. The requirement of alignment is a restriction imposed by traditional regression methods due to their failure to represent relations between data objects directly. Inductive logic programming (ILP) is a class of machine‐learning methods able to describe relational data directly. We propose a new methodology which is aimed at using the richness in molecular interaction fields (MIFs) without being restricted by any alignment procedure. A set of MIFs is computed and further compressed by finding their minima corresponding to the sites of strongest interaction between a molecule and the applied test probe. ILP uses these minima to build easily interpretable rules about activity expressed as pharmacophore rules in the powerful language of first‐order logic. We use a set of previously published inhibitors of factor Xa of the benzamidine family to discuss the problems, requirements and advantages of the new methodology. Copyright © 2007 John Wiley & Sons, Ltd.     

Molecular modeling on pyrimidine-urea inhibitors of TNF-α production: an integrated approach using a combination of molecular docking, classification techniques, and 3D-QSAR CoMSIA

Molecular docking, classification techniques, and 3D-QSAR CoMSIA were combined in a multistep framework with the ultimate goal of identifying potent pyrimidine-urea inhibitors of TNF-α production. Using the crystal structure of p38α, all the compounds were docked into the enzyme active site. The docking pose of each compound was subsequently used in a receptor-based alignment for the generation of the CoMSIA fields. "Active" and "inactive" compounds were used to build a Random Tree classification model using the docking score and the CoMSIA fields as input parameters. Domain of applicability indicated the compounds for which activity estimations can be accepted with confidence. For the active compounds, a 3D-QSAR CoMSIA model was subsequently built to accurately estimate the IC(50) values. This novel multistep framework gives insight into the structural characteristics that affect the binding and the inhibitory activity of these analogues on p38α MAP kinase, and it can be extended to other classes of small-molecule inhibitors. In addition, the simplicity of the proposed approach provides expansion to its applicability such as in virtual screening procedures.     

具有除草活性的α-氧代膦酸衍生物研究进展

简要的总结了十三个系列的α-氧代膦酸衍生物的合成和生物活性的研究进展。为了获得具有较高除草活性及商品化潜力的新型膦酸脂,尝试采用生物合理设计的方法设计新型的丙酮酸脱氢酶系抑制剂,通过在合成、生物测定、机理及生物化学研究的基础上进行构效关系及结构优化的研究,发现α-(取代苯氧乙酰氧基)烃基膦酸酯为一类兼具有良好除草活性和植调活性的先导母体,研究证明该类化合物为丙酮酸脱氢酶系的强抑制剂。