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An approach that combines NMR spectroscopy and inductively coupled plasma mass spectrometry (ICP-MS) and advanced tensor decomposition algorithms with state-of-the-art deep learning procedures was applied for the classification of Croatian continental sparkling wines by their geographical origin. It has been demonstrated that complex high-dimensional NMR or ICP-MS data cannot be classified by higher-order tensor decomposition alone. Extension of the procedure by deep reinforcement learning resulted in an exquisite neural network predictive model for the classification of sparkling wines according to their geographical origin. A network trained on half of the sample set was able to classify even 94% of all samples. The model can particularly be useful in cases where the number of samples is limited and when simpler statistical methods fail to produce reliable data. The model can further be exploited for the identification and differentiation of sparkling wines including a high potential for authenticity or quality control.

A series of 46 Cinchona alkaloid derivatives that differ in positions of fluorine atom(s) in the molecule were synthesized and tested as human acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitors. All tested compounds reversibly inhibited AChE and BChE in the nanomolar to micromolar range; for AChE, the determined enzyme-inhibitor dissociation constants (Ki) ranged from 3.9–80 µM, and 0.075–19 µM for BChE. The most potent AChE inhibitor was N-(para-fluorobenzyl)cinchoninium bromide, while N-(meta-fluorobenzyl)cinchonidinium bromide was the most potent BChE inhibitor with Ki constant in the nanomolar range. Generally, compounds were non-selective or BChE selective cholinesterase inhibitors, where N-(meta-fluorobenzyl)cinchonidinium bromide was the most selective showing 533 times higher preference for BChE. In silico study revealed that twenty-six compounds should be able to cross the blood-brain barrier by passive transport. An extensive machine learning procedure was utilized for the creation of multivariate linear regression models of AChE and BChE inhibition. The best possible models with predicted R2 (CD-derivatives) of 0.9932 and R2(CN-derivatives) of 0.9879 were calculated and cross-validated. From these data, a smart guided search for new potential leads can be performed. These results pointed out that quaternary Cinchona alkaloids are the promising structural base for further development as selective BChE inhibitors which can be used in the central nervous system.

Bacterial infections that do not respond to current treatments are increasing, thus there is a need for the development of new antibiotics. Series of 20 N-substituted quaternary salts of cinchonidine (CD) and their quasi-enantiomer cinchonine (CN) were prepared and their antimicrobial activity was assessed against a diverse panel of Gram-positive and Gram-negative bacteria. All tested compounds showed good antimicrobial potential (minimum inhibitory concentration (MIC) values 1.56 to 125.00 μg/mL), proved to be nontoxic to different human cell lines, and did not influence the production of reactive oxygen species (ROS). Seven compounds showed very strong bioactivity against some of the tested Gram-negative bacteria (MIC for E. coli and K. pneumoniae 6.25 μg/mL; MIC for P. aeruginosa 1.56 μg/mL). To establish a connection between antimicrobial data and potential energy surfaces (PES) of the compounds, activity/PES models using principal components of the disc diffusion assay and MIC and data towards PES data were built. An extensive machine learning procedure for the generation and cross-validation of multivariate linear regression models with a linear combination of original variables as well as their higher-order polynomial terms was performed. The best possible models with predicted R2(CD derivatives) = 0.9979 and R2(CN derivatives) = 0.9873 were established and presented. This activity/PES model can be used for accurate prediction of activities for new compounds based solely on their potential energy surfaces, which will enable wider screening and guided search for new potential leads. Based on the obtained results, N-quaternary derivatives of Cinchona alkaloids proved to be an excellent scaffold for further optimization of novel antibiotic species.

The treatment of central nervous system (CNS) diseases related to the decrease of neurotransmitter acetylcholine in neurons is based on compounds that prevent or disrupt the action of acetylcholinesterase and butyrylcholinesterase. A series of thirteen quinuclidine carbamates were designed using quinuclidine as the structural base and a carbamate group to ensure the covalent binding to the cholinesterase, which were synthesized and tested as potential human acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitors. The synthesized compounds differed in the substituents on the amino and carbamoyl parts of the molecule. All of the prepared carbamates displayed a time-dependent inhibition with overall inhibition rate constants in the 103 M−1 min−1 range. None of the compounds showed pronounced selectivity for any of the cholinesterases. The in silico determined ability of compounds to cross the blood–brain barrier (BBB) revealed that six compounds should be able to pass the BBB by passive transport. In addition, the compounds did not show toxicity toward cells that represented the main models of individual organs. By machine learning, the most optimal regression models for the prediction of bioactivity were established and validated. Models for AChE and BChE described 89 and 90% of the total variations among the data, respectively. These models facilitated the prediction and design of new and more potent inhibitors. Altogether, our study confirmed that quinuclidinium carbamates are promising candidates for further development as CNS-active drugs, particularly for Alzheimer’s disease treatment.

NMR spectroscopy in combination with statistical methods was used to study vacuum residues and vacuum gas oils from 32 crude oils of different origin. Two chemometric metodes were applied. Firstly, principal component analysis on complete spectra was used to perform classification of samples and clear distinction between vacuum residues and vacuum light and heavy gas oils were obtained. To quantitatively predict the composition of asphaltenes, principal component regression models using areas of resonance signals spaned by 11 frequency bins of the 1H NMR spectra were build. The first 5 principal components accounted for more than 94 % of variations in the input data set and coefficient of determination for correlation between measured and predicted values was R2 = 0.7421. Although this value is not significant, it shows the underlying linear dependence in the data. Pseudo two-dimensional DOSY NMR experiments were used to assess the composition and structural properties of asphaltenes in a selected crude oil and its vacuum residue on the basis of their different hydrodynamic behavior and translational diffusion coefficients. DOSY spectra showed the presence of several asphaltene aggregates differing in size and interactions they formed. The obtained results have shown that NMR techniques in combination with chemometrics are very useful to analyze vacuum residues and vacuum gas oils. Furthermore, we expect that our ongoing investigation of asphaltenes from crude oils of different origin will elucidate in more details composition, structure and properties of these complex molecular systems.

This paper describes the synthesis and anticholinesterase potency of Cinchona-based alkaloids; ten quaternary derivatives of cinchonines and their corresponding pseudo-enantiomeric cinchonidines. The quaternization of quinuclidine moiety of each compound was carried out with groups diverse in their size: methyl, benzyl and differently meta- and para-substituted benzyl groups. All of the prepared compounds reversibly inhibited human butyrylcholinesterase and acetylcholinesterase with Ki constants within nanomolar to micromolar range. Five cinchonidine derivatives displayed 95-510 times higher inhibition selectivity to butyrylcholinesterase over acetylcholinesterase and four were potent butyrylcholinesterase inhibitors with Ki constants up to 100 nM, of which N-para-bromobenzyl cinchonidinium bromide can be considered a lead for further modifications and optimizations for possible use in the treatment of neurodegenerative diseases.

Developing new antibiotics is currently very important since antibiotic resistance is one of the biggest problems of global health today. In the search for a new class of potential antimicrobial agents, ten new compounds were designed and synthesized based on the quinuclidinium heterocyclic core and the oxime functional group. The antimicrobial activity was assessed against a panel of representative gram-positive and gram-negative bacteria. All compounds demonstrated potent activity against the tested microorganisms, with the minimum inhibitory concentration (MIC) values ranging from 0.25 to 256.00 μg/mL. Among the tested compounds, two quaternary compounds, para-N-chlorobenzyl and meta-N-bromobenzyl quinuclidinium oximes, displayed the most potent and broad-spectrum activity against both gram-positive and gram-negative bacterial strains (MIC values from 0.25 to 4.00 μg/mL), with the lowest value for the important multidrug resistant gram-negative pathogen Pseudomonas aeruginosa. In the case of Klebsiella pneumoniae, activity of those compounds are 256-fold and 16-fold better than gentamicin, respectively. MTT assays showed that compounds are nontoxic for human cell lines. Multi-way analysis was used to separately reduce dimensionality of quantum chemical data and biological activity data to obtain a regression model and the required parameters for the enhancement of biological activity.

Harmonic vibrational frequencies are computed using second-order Møller−Plesset perturbation theory (MP2) with and without local (LMP2) and density fitting (DF) approximations. Results for a test set of 17 small and medium size molecules (366 normal modes) are presented, and frequency scaling factors for LMP2 in combination with two different basis sets are determined. Comparison of the MP2 and LMP2 frequencies with experimental data reveals that the introduction of local approximations leads to a slightly better agreement with experiment. This is attributed to the reduction of basis set superposition errors in local calculations. Introduction of DF approximations within the LMP2 formalism leads to negligible deviations but significantly reduces the computational cost. These facts extend the applicability of the method to larger systems with large basis sets. As an example, the method is applied to a full DF-LMP2/cc-pVTZ frequency calculation for testosterone (49 atoms). [ABSTRACT FROM AUTHOR]