The computer-aided drug design is an important tool in modern medicinal chemistry. Molecular lipophilicity, usually quantified as log P, is an important molecular characteristic in medicinal chemistry and also in rationalized drug design. The log P coefficient is well-known as one of the principal parameters for the estimation of lipophilicity of chemical compounds and determines their pharmacokinetic properties. This parameter has been measured using known experimental methods, but recently huge progress in determination of log P using computational chemistry methods is observed. The number of methodological publications about lipophilicity predictions has gradually increased over the last ten years, but the number of programs available for an on-line prediction of this important parameter remains limited. This paper presents some of log P prediction methods and very popular programs connected to this topic. The prediction of log P is highly important for the pharmaceutical industry since it limits time-consuming experiments to measure log P required to optimize pharmacodynamic and pharmacokinetic properties of hits and leads. Development of the methods reviewed in this paper concerning log P prediction seems to be a significant tendency in the modern pharmaceutical industry.

Metabolomics is a rapidly developing branch of science that concentrates on identifying biologically active molecules with potential biomarker properties. To define the best biomarkers for diseases, metabolomics uses both models (in vitro, animals) and human, as well as, various techniques such as mass spectroscopy, gas chromatography, liquid chromatography, infrared and UV–VIS spectroscopy and nuclear magnetic resonance. The last one takes advantage of the magnetic properties of certain nuclei, such as 1H, 13C, 31P, 19F, especially their ability to absorb and emit energy, what is crucial for analyzing samples. Among many spectroscopic NMR techniques not only one-dimensional (1D) techniques are known, but for many years two-dimensional (2D, for example, COSY, DOSY, JRES, HETCORE, HMQS), three-dimensional (3D, DART-MS, HRMAS, HSQC, HMBC) and solid-state NMR have been used. In this paper, authors taking apart fundamental division of nuclear magnetic resonance techniques intend to shown their wide application in metabolomic studies, especially in identifying biomarkers.

Molecular hydrophobicity (lipophilicity), usually quantified as log P where P is the partition coefficient, is an important molecular characteristic in medicinal chemistry and drug design. The log P coefficient is one of the principal parameters for the estimation of lipophilicity of chemical compounds and pharmacokinetic properties. The understanding of log P parameter in the undergraduate medicinal chemistry course seems to be a pitfall for students. This parameter has typically been measured using experimental methods, but recently, log P has been determined using computational methods. The number of publications about lipophilicity predictions has gradually increased over the last 10 years, but the number of programs available for an online prediction of this important parameter remains limited. An interesting tool for calculation of log P coefficients is presented: the Virtual Computational Chemistry Laboratory (VCCLAB) package. The package includes the ALOGPS 2.1 program suitable for log P calculations. This software is accessible online and may be easily mastered by the undergraduate medicinal chemistry student.

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In this Communication, we present experimental studies that put new insight into the puzzling nature of the Debye relaxation found in the supercooled liquid state of racemic ibuprofen. The appearance of D-relaxation in the loss spectra of non-hydrogen bonding methylated derivate of ibuprofen has proven that Debye relaxation is related solely with conformational changes of the carboxyl group, termed in this paper as synperiplanar-antiperiplanar. Our studies indicate that the presence of hydrogen bonding capabilities is not here the necessary condition to observe Debye process, however, their occurrence might strongly influence α- and D-relaxations dynamics. Interestingly, the activation energy of the D-process in ibuprofen methyl ester on approaching T(g) was found to be perfectly consistent with that reported for ibuprofen by Affouard and Correia [J. Phys. Chem. B 114, 11397-11402 (2010)] (~39 kJ/mol). Finally, IR measurements suggest that the equilibrium between conformers concentration depends on time and temperature, which might explain why the appearance of D-relaxation in supercooled ibuprofen depends on thermal history of the sample.