Internet software for the calculation of the lipophilicity and aqueous solubility of chemical compounds

In this paper we describe an Internet Java-based technology that allows scientists to make their analytical software available worldwide. The implementation of this technology is exemplified by programs for the calculation of the lipophilicity and water solubility of chemical compounds available at http://www.lnh.unil.ch/~itetko/logp. Both these molecular properties are key parameters in quantitative structure-activity relationship studies and are used to provide invaluable information for the overall understanding of the uptake distribution, biotransformation, and elimination of a wide variety of chemicals. The compounds can be analyzed in batch or single-compound mode. The single-compound analysis offers the possibility to compare our results with several popular lipophilicity calculation methods, including CLOGP, KOWWIN, and XLOGP. The chemical compounds are analyzed according to SMILES line notation that can be prepared with the JME molecular editor of Peter Ertl. Conversion to SMILES from 56 formats is also available using the molecular structure information interchange hub developed by Pat Walters and Matt Stahl.     

Heterocycles 51: Liphophilicity investigation of some thiazole chalcones and aurones by experimental and theoretical methods

Reversed‐phase thin‐layer chromatography and reversed‐phase high‐performance liquid chromatography were used for lipophilicity determination of a library of 30 thiazole chalcones and aurones previously synthetized in our laboratory. The experimental lipophilicity data have been compared with theoretical lipophilicity parameters estimated by various computational methods. Good correlations between the experimental and calculated lipophilicity parameters have been found for both investigated classes of compounds. Correlations between the lipophilicity of the thiazole chalcones and aurones and their antiproliferative activity were discussed. The methodologies and data gathered in this study will contribute to the lipophilicity studies of chalcones and aurones derivatives, two important classes of compounds in medicinal chemistry.     

Determination of lipophilicity of alpha-(4-phenylpiperazine) derivatives of N-benzylamides using chromatographic and computational methods

This paper describes the evaluation of lipophilicity of alpha-(4-phenylpiperazine) derivatives of N-benzylamides. We employed reversed-phase thin-layer chromatography (RP-TLC) and reversed-phase high performance liquid chromatography (RP-HPLC) as experimental methods, using mixtures of acetonitrile and water as the mobile phases with addition of 0.1%TFA in the HPLC experiments. Retention parameters (R(M)) and capacity factors (log k) determined by applying these methods were linearly dependent on the acetonitrile concentration and enabled us to estimate the relative lipophilicity factors: R(M0) and log k(0). These factors were compared with the calculated partition coefficients C log P obtained using several software packages. The results indicate that both experimental methods (RP-TLC and RP-HPLC) yielded similar results, and these methods enable determining the lipophilicity of alpha-(4-phenylpiperazine) derivatives of N-benzylamides. Significant correlations were found between log P values calculated by Pallas, ALOGPS and C log P Chem3D programs and the experimental data.     

PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry

A number of different approaches have been described to identify proteins from tandem mass spectrometry (MS/MS) data. The most common approaches rely on the available databases to match experimental MS/MS data. These methods suffer from several drawbacks and cannot be used for the identification of proteins from unknown genomes. In this communication, we describe a new de novo sequencing software package, PEAKS, to extract amino acid sequence information without the use of databases. PEAKS uses a new model and a new algorithm to efficiently compute the best peptide sequences whose fragment ions can best interpret the peaks in the MS/MS spectrum. The output of the software gives amino acid sequences with confidence scores for the entire sequences, as well as an additional novel positional scoring scheme for portions of the sequences. The performance of PEAKS is compared with Lutefisk, a well-known de novo sequencing software, using quadrupole-time-of-flight (Q-TOF) data obtained for several tryptic peptides from standard proteins.     

Smoothing and differentiation of thermogravimetric data of biomass materials

Due to the experimental errors, the chemical effect of minor reactions, and some physical effects of heat and mass transfer, there usually exists much noise in the mass loss data resulted from thermal decomposition experiments, and thus high quality smoothing algorithm plays an important role in obtaining reliable derivative thermogravimetric (DTG) curves required for differential kinetic analysis. In this paper three smoothing methods, i.e. Moving Average smoothing, Gaussian smoothing, and Vondrak smoothing, are investigated in detail for pre-treatment of biomass decomposition data to obtain the DTG curves, and the smoothing results are compared. It is concluded that by choosing reasonable smoothing parameters based on the spectrum analysis of the data, the Gaussian smoothing and Vondrak smoothing can be reliably used to obtain DTG curves. The kinetic parameters calculated from the original TG curves and smoothed DTG curves have excellent agreement, and thus the Gaussian and Vondrak smoothing algorithms can be used directly and accurately in kinetic analysis.This work was sponsored by National Natural Science Foundation of China under Grants 50346038 and 50323005, the China NKBRSF project (No. 2001CB409600), the Anhui Excellent Youth Scientist Fundation (2004–2005), the Specialized Research Fund for the Doctoral Program of Higher Education and the National Key Technologies R&D Programme (2001BA510B09-03).     

Improved Lipophilicity and Aqueous Solubility Prediction with Composite Graph Neural Networks

The accurate prediction of molecular properties, such as lipophilicity and aqueous solubility, are of great importance and pose challenges in several stages of the drug discovery pipeline. Machine learning methods, such as graph-based neural networks (GNNs), have shown exceptionally good performance in predicting these properties. In this work, we introduce a novel GNN architecture, called directed edge graph isomorphism network (D-GIN). It is composed of two distinct sub-architectures (D-MPNN, GIN) and achieves an improvement in accuracy over its sub-architectures employing various learning, and featurization strategies. We argue that combining models with different key aspects help make graph neural networks deeper and simultaneously increase their predictive power. Furthermore, we address current limitations in assessment of deep-learning models, namely, comparison of single training run performance metrics, and offer a more robust solution.     

A homology/ab initio hybrid algorithm for sampling near‐native protein conformations

One of the major challenges for protein tertiary structure prediction strategies is the quality of conformational sampling algorithms, which can effectively and readily search the protein fold space to generate near‐native conformations. In an effort to advance the field by making the best use of available homology as well as fold recognition approaches along with ab initio folding methods, we have developed Bhageerath‐H Strgen, a homology/ab initio hybrid algorithm for protein conformational sampling. The methodology is tested on the benchmark CASP9 dataset of 116 targets. In 93% of the cases, a structure with TM‐score ≥ 0.5 is generated in the pool of decoys. Further, the performance of Bhageerath‐H Strgen was seen to be efficient in comparison with different decoy generation methods. The algorithm is web enabled as Bhageerath‐H Strgen web tool which is made freely accessible for protein decoy generation ( http://www.scfbio‐iitd.res.in/software/Bhageerath‐HStrgen1.jsp ). © 2013 Wiley Periodicals, Inc.     

Lipophilicity analysis of newly synthetized quinobenzothiazines by use of TLC

Lipophilicity is a very important property of chemical compound taking into consideration in drugs design. Relationships between biological activity, among others lipophilicity, and chemical structure (QSAR) of the compound are very often used by researches. Especially important is the kind of substituents connected to the basic structural fragment and how it changes the lipophilicity of the compound. The aim of this study was to determine the parameters of lipophilicity of quinobenzothiazine derivatives using reversed phase - thin-layer chromatography (RP-TLC), which would enable one to determine the structure–activity relationship. The objective of our work is a series of 15 newly synthetized quinobenzothiazines. They were analyzed by thin-layer chromatography (TLC) with the use of two different mobile phases consisting of methanol or acetone as organic modifiers. For all compounds investigated, the values of lipophilicity obtained from computational method were also determined. Cluster analysis was carried out too for all data of lipophilicity obtained. Low correlation was found between values of experimental lipophilicity and lipophilicity from computational methods for newly synthetized compounds.     

Post-optimization of Py-GC/MS data: A case study using a new digital chemical noise reduction filter (NOISERA) to enhance the data quality utilizing OpenChrom mass spectrometric software

Noise is a known drawback in the mass spectrometric analysis of chromatographic data. Different techniques exist to avoid or to remove noise before and after data acquisition. Noise occurs in two different types, electronic and chemical. The former can be lowered by applying smoothing filters. The latter needs more sophisticated methods since the distinction between chemical noise and real chromatographic data is not straightforward. Several approaches have been published with different scopes and algorithms. This work describes a new algorithm (NOISERA - Noise Reduction Algorithm) for a dynamic reduction of chemical noise in mass spectrometric, chromatographic data of nominal mass resolution. It offers an approach to detect and to reduce chemical noise with a minimum of required user interaction and adjustments. Due to the assumption, that chemical noise varies in its intensity and is dependent of the time, the algorithm tries to calculate and reduce noise locally along the retention time axis. Electronic noise will not be covered by the presented algorithm. It can be reduced by applying further filters, since OpenChrom provides methods for smoothing.The presented algorithm NOISERA is capable of reducing noise with minimal user interaction. It combines existing ideas and extends them to achieve a parameter-free chemical noise detection and reduction. NOISERA improves the signal-to-noise ratio of peaks, but as a drawback of all chemical noise filters, it tends to remove either too much, respectively, not enough data from chromatograms than necessary. That is why the algorithm should not be used without supervision of an analyst. The noise reduction filter NOISERA is available as a plug-in for OpenChrom and is released under the Eclipse Public License 1.0 (EPL). OpenChrom is available free of charge at http://www.openchrom.net. The noise reduction algorithm NOISERA is included in the latest OpenChrom release.     

Heuristic lipophilicity potential for computer-aided rational drug design

In this contribution we suggest a heuristic molecular lipophilicitypotential (HMLP), which is a structure-based technique requiring noempirical indices of atomic lipophilicity. The input data used in thisapproach are molecular geometries and molecular surfaces. The HMLP is amodified electrostatic potential, combined with the averaged influences fromthe molecular environment. Quantum mechanics is used to calculate theelectron density function (r) and the electrostatic potential V(r), andfrom this information a lipophilicity potential L(r) is generated. The HMLPis a unified lipophilicity and hydrophilicity potential. The interactions ofdipole and multipole moments, hydrogen bonds, and charged atoms in amolecule are included in the hydrophilic interactions in this model. TheHMLP is used to study hydrogen bonds and water–octanol partitioncoefficients in several examples. The calculated results show that the HMLPgives qualitatively and quantitatively correct, as well as chemicallyreasonable, results in cases where comparisons are available. Thesecomparisons indicate that the HMLP has advantages over the empiricallipophilicity potential in many aspects. The HMLP is a three-dimensional andeasily visualizable representation of molecular lipophilicity, suggested asa potential tool in computer-aided three-dimensional drug design.     

Hydrogen bonds: a comparison of semiempirical and ab initio treatments

H-bonding interactions calculated using the AM1, PM3 and SAM1 semiempirical molecular orbital methods are compared with the best available ab initio calculations for several intermolecular interactions of interest: acetic acid dimers, water/ acetylene, water/HCN, formaldehyde/acetylene, formaldehyde/HCN, ozone/acetylene, ozone/HCN, acetylacetone, melamine/ cyanuric acid, and nitromethane/ammonia. Experimental values are also presented where available. The energetic comparisons are based upon enthalpies of interaction from the ab initio calculations after counterpoise and vibrational corrections have been applied. Overall, AM1 seems to do best, except for O---H…O interactions, where none of the three methods excel.     

High-throughput evaluation of lipophilicity and acidity by new gradient HPLC methods

There is a need for fast testing of drug candidates for properties of pharmacokinetics and pharmacodynamics importance, in particular lipophilicity and acidity. These two parameters can conveniently be estimated by gradient reversed-phase HPLC. Appropriate conventional organic solvent gradient and the new pH gradient HPLC procedures are presented. The chromatographic parameter of lipophilicity, log kw, can be determined from two organic solvent gradient runs instead of 6-8 runs necessary in the standard isocratic (polycratic) approach. The newly introduced pH gradient reversed-phase HPLC consists in a programmed increase during the chromatographic run of the eluting power of the mobile phase with regards to ionizable analytes. The eluting strength of the mobile phase increases due to its increasing (in case of acidic analytes) or decreasing (basic analytes) pH, whereas the content of organic modifier remains constant. It has been theoretically and experimentally demonstrated that the pKa and log kw values can be evaluated based on retention data from a pH gradient run, combined with appropriate data from two organic solvent gradient runs. The gradient HPLC-derived log kw parameters correlate well with analogous parameters determined isocratically as well as with reference lipophilicity parameter log P (logarithm of n-octanol/water partition coefficient). Also, the HPLC-derived pKa parameters correlate to the literature pKa values (w(w)pKa), conventionally determined by titrations in water. The approach described allows rapid and high-throughput assessment of log kw and pKa for large series of drugs candidates, also when the analytes are available in a form of mixture, e.g. produced by combinatorial synthesis.     

Chemometric methods in NMR spectroscopic analysis of food products

The main achievements of high-resolution NMR spectroscopy in combination with different chemometric methods in the analysis of food products in the last 40 years are reviewed. The essence of chemometric methods used for the analysis and interpretation of NMR spectra is briefly described. Sample preparation for NMR-spectroscopic analysis is characterized. Methods for the mathematical treatment of NMR spectra (smoothing, Fourier transformation, bucketing, normalization, and selection of spectral ranges) are considered. Currently available methods for the suppression of the signals of macrocomponents, including those for the simultaneous suppression of several signals, are described. The results are illustrated based on examples of analysis of different classes of foodstuffs and beverages with the use of NMR spectroscopy and chemometric methods for classification and discrimination (geographical and botanical origin as well as validating checking the brand authenticity).     

Relationship between physicochemical properties,lipophilicity parameters,and local anesthetic activity of dibasic esters of phenylcarbamic acid

The basic physicochemical properties, lipophilicity parameters of dibasic alkyloxy-substituted phenylcarbamic acids were estimated. For the prepared set of compounds the experimentally obtained solubility, acidity, and lipophilicity parameters were correlated with those computed using various computer programs based on the associative artificial neural network and fragmental methods. The results of pharmacological evaluation were used as entry data for the complex correlations.     

Virtual screening using binary kernel discrimination: effect of noisy training data and the optimization of performance

Binary kernel discrimination (BKD) uses a training set of compounds, for which structural and qualitative activity data are available, to produce a model that can then be applied to the structures of other compounds in order to predict their likely activity. Experiments with the MDL Drug Data Report database show that the optimal value of the smoothing parameter, and hence the predictive power of BKD, is crucially dependent on the number of false positives in the training set. It is also shown that the best results for BKD are achieved using one particular optimization method for the determination of the smoothing parameter that lies at the heart of the method and using the Jaccard/Tanimoto coefficient in the kernel function that is used to compute the similarity between a test set molecule and the members of the training set.     

A new hybrid algorithm for finding the lowest minima of potential surfaces: approach and application to peptides

A new algorithm is presented for finding the global minimum, and other low-lying minima, of a potential energy surface (PES) of biological molecules. The algorithm synergetically combines three well-known global optimization methods: the diffusion equation method (DEM), which involves smoothing the PES; a simulated annealing (SA) algorithm; and evolutionary programming (EP), whose population-oriented approach allows for a parallel search over different regions of the PES. Tests on five peptides having between 6 and 9 residues show that the code implementing the new combined algorithm is efficient and is found to outperform the constituent methods, DEM and SA. Results of the algorithm, in the gas phase and with the GBSA implicit solvent model, are compared with crystallographic data for the test peptides; good accord is found in all cases. Also, for all but one of the examples, our hybrid algorithm finds a minimum deeper than those obtained by a very extensive scan. TINKERs implementation of the OPLS-AA force field is employed for the structure prediction. The results show that the new algorithm is a powerful structure predictor, when a reliable potential function is available. Our implementation of the algorithm is time-efficient, and requires only modest computational resources. Work is underway on applications of the new algorithm to structural prediction of proteins and other biological macro-molecules.     

Determination of the lipophilicity of some anti-hypoxia drugs: Comparison of TLC and HPLC methods

Summary The lipophilicity of 14 anti-hypoxia drugs has been determined by reversed phase thin-layer (RPTLC) and reversed phase high performance liquid chromatography (RPHPLC) in eluent systems containing different concentrations of acetonitrile and potassium dihydrogen phosphate. There was significant correlation between lipophilicity and the specific hydrophobic surface area of the drugs in RPTLC, indicating that the drugs behave as an homologous series of compounds. In RPTLC the concentration of buffer has a negligible effect on the retention of the drugs whereas in RPHPLC the buffer concentration influenced the retention. This discrepancy can be explained by the lower sensitivity of RPTLC. There was strong correlation between lipophilicity values determined by both methods, proving that both are suitable for the determination of molecular lipophilicity.