Prediction of ultraviolet spectral absorbance using quantitative structure-property relationships

Electronic investigation and spectroscopic analysis using DFT with the long-range dispersion correction on the six lowest conformers of 2.2.3-trimethyl pentane

The conformational stability and internal rotation barriers, HOMO-LUMO gap and related properties, molecular static polarizability and hyperpolarizability parameters, and the NBO delocalization energies associated with the internal charge transfer (ICT) of 2.2.3-trimethylpentane in the ground state were carried out taking into account the long range dispersion correction through CAM-B3LYP and WB97XD levels at aug-cc-pvtz basis set. The six lowest conformations were differentiated by a deep and multiple spectroscopic investigation. The ultraviolet-visible (UV-Vis) absorption bands are assigned using molecular orbital data obtained by TD-WB97XD/aug-cc-pvtz calculations, and carbon ¹³C NMR signal peaks have been assigned using GIAO-WB97XD/aug-cc-pvtz method. In addition, the normal mode calculations of the most and less stable conformers using a scaled force field in terms of non-redundant local symmetry coordinates have been confronted to the experimental vibrational spectra temperature dependency.

Applying Discriminant and Cluster Analyses to Separate Allergenic from Non-allergenic Proteins

As a result of increased healthcare requirements and the introduction of genetically modified foods, the problem of allergies is becoming a growing health problem. The concept of allergies has prompted the use of new methods such as genomics and proteomics to uncover the nature of allergies. In the present study, a selection of 1400 food proteins was analysed by PLS-DA (Partial Least Square-based Discriminant Analysis) after suitable transformation of structural parameters into uniform vectors. Then, the resulting strings of different length were converted into vectors with equal length by Auto and Cross-Covariance (ACC) analysis. Hierarchical and non-hierarchical (K-means) Cluster Analysis (CA) was also performed in order to reach a certain level of separation within a small training set of plant proteins (16 allergenic and 16 non-allergenic) using a new three-dimensional descriptor based on surface protein properties in combination with amino acid hydrophobicity scales. The novelty of the approach in protein differentiation into allergenic and non-allergenic classes is described in the article.

The general goal of the present study was to show the effectiveness of a traditional chemometric method for classification (PLS–DA) and the options of Cluster Analysis (CA) to separate by multivariate statistical methods allergenic from non-allergenic proteins.

Antiprotozoal glutathione derivatives with flagellar membrane binding activity against T. brucei rhodesiense

A new series of N-substituted S-(2,4-dinitrophenyl)glutathione dibutyl diesters were synthesized to improve in vitro anti-protozoal activity against the pathogenic parasites Trypanosoma brucei rhodesiense, Trypanosoma cruzi and Leishmania donovani. The results obtained indicate that N-substituents enhance the inhibitory properties of glutathione diesters whilst showing reduced toxicity against KB cells as in the cases of compounds 5, 9, 10, 16, 18 and 19. We suggest that the interaction of N-substituted S-(2,4-dinitrophenyl) glutathione dibutyl diesters with T. b. brucei occurs mainly by weak hydrophobic interactions such as London and van der Waals forces. A QSAR study indicated that the inhibitory activity of the peptide is associated negatively with the average number of C atoms, N_C and positively to S_ZX, the ZX shadow a geometric descriptor related to molecular size and orientation of the compound. HPLC-UV studies in conjunction with optical microscopy indicate that the observed selectivity of inhibition of these compounds against bloodstream form T. b. brucei parasites in comparison to L. donovani under the same conditions is due to intracellular uptake via endocytosis in the flagellar pocket.

Current status and prospects of computational resources for natural product dereplication: a review

Research in natural products has always enhanced drug discovery by providing new and unique chemical compounds. However, recently, drug discovery from natural products is slowed down by the increasing chance of re-isolating known compounds. Rapid identification of previously isolated compounds in an automated manner, called dereplication, steers researchers toward novel findings, thereby reducing the time and effort for identifying new drug leads. Dereplication identifies compounds by comparing processed experimental data with those of known compounds, and so, diverse computational resources such as databases and tools to process and compare compound data are necessary. Automating the dereplication process through the integration of computational resources has always been an aspired goal of natural product researchers. To increase the utilization of current computational resources for natural products, we first provide an overview of the dereplication process, and then list useful resources, categorizing into databases, methods and software tools and further explaining them from a dereplication perspective. Finally, we discuss the current challenges to automating dereplication and proposed solutions.

Chemometric analysis of correlations between electronic absorption characteristics and structural and/or physicochemical parameters for ampholytic substances of biological and pharmaceutical relevance

Forty ampholytic compounds of biological and pharmaceutical relevance were subjected to chemometric analysis based on unsupervised and supervised learning algorithms. This enabled relations to be found between empirical spectral characteristics derived from electronic absorption data and structural and physicochemical parameters predicted by quantum chemistry methods or phenomenological relationships based on additivity rules. It was found that the energies of long wavelength absorption bands are correlated through multiparametric linear relationships with parameters reflecting the bulkiness features of the absorbing molecules as well as their nucleophilicity and electrophilicity. These dependences enable the quantitative analysis of spectral features of the compounds, as well as a comparison of their similarities and certain pharmaceutical and biological features. Three QSPR models to predict the energies of long-wavelength absorption in buffers with pH=2.5 and pH=7.0, as well as in methanol, were developed and validated in this study. These models can be further used to predict the long-wavelength absorption energies of untested substances (if they are structurally similar to the training compounds).

QSPR modeling of UV absorption intensities

Literature UV absorption intensities at 260 nm and 25 degrees C in water of a diverse set of 805 organic compounds when analyzed by CODESSA Pro software using an initial pool of 800 + descriptors provide a significant QSPR correlation (R (2) = 0.692). Concurrently, a neural networks approach was used to develop a corresponding nonlinear model. The descriptors appearing in these models are discussed with respect to the physical nature of the UV absorption phenomenon.

Analytical Tools and Approaches for Metabolite Identification in Early Drug Discovery

Determination of the chemical structures of metabolites is a critical part of the early pharmaceutical discovery process. Understanding the structures of metabolites is useful both for optimizing the metabolic stability of a drug as well as rationalizing the drug safety profile. This review describes the current state of the art in this endeavor. The likely outcome of metabolism is first predicted by comparison to the literature. Then metabolites are synthesized in a variety of in vitro systems. The various approaches to LC/UV/MS are applied to learn information about these metabolites and structure hypotheses are made. Structures are confirmed by synthesis or NMR. The special topic of reactive metabolite structure determination is briefly addressed.

Prediction of UV and ESI−MS Signal Intensities

All major pharmaceutical companies maintain large collections of compounds that are used either for screening against biological targets or as synthetic precursors. The quality assessment of these compounds is typically done by liquid chromatography combined with mass spectroscopy (LC/MS) and UV purity control. To facilitate the analysis of the analytical data, we have built computational models to predict UV and MS signal intensities under experimental LC/MS conditions. The discriminant partial-least-squares technique was used for classifying compounds into those most likely to yield a MS signal and others where the signal is below the detection limit (94% and 88% correct predictions, respectively). In the case of UV prediction, we compared this statistical linear-regression technique to a knowledge-based approach. A combination of both techniques proved to be the most reliable (96/98% correct predictions of UV-active/ UV-inactive compounds). Both models have been incorporated into the automated compound integrity profiling at F. Hoffmann-La Roche.