Extreme Gradient Boosting Combined with Conformal Predictors for Informative Solubility Estimation.

We used the extreme gradient boosting (XGB) algorithm to predict the experimental solubility of chemical compounds in water and organic solvents and to select significant molecular descriptors. The accuracy of prediction of our forward stepwise top-importance XGB (FSTI-XGB) on curated solubility data sets in terms of RMSE was found to be 0.59-0.76 Log(S) for two water data sets, while for organic solvent data sets it was 0.69-0.79 Log(S) for the Methanol data set, 0.65-0.79 for the Ethanol data set, and 0.62-0.70 Log(S) for the Acetone data set. That was the first step. In the second step, we used uncurated and curated AquaSolDB data sets for applicability domain (AD) tests of Drugbank, PubChem, and COCONUT databases and determined that more than 95% of studied ca. 500,000 compounds were within the AD. In the third step, we applied conformal prediction to obtain narrow prediction intervals and we successfully validated them using test sets' true solubility values. With prediction intervals obtained in the last fourth step, we were able to estimate individual error margins and the accuracy class of the solubility prediction for molecules within the AD of three public databases. All that was possible without the knowledge of experimental database solubilities. We find these four steps novel because usually, solubility-related works only study the first step or the first two steps.

Chemical Evaluation, Antioxidant, Antiproliferative, Anti-Inflammatory and Antibacterial Activities of Organic Extract and Semi-Purified Fractions of the Adriatic Sea Fan, Eunicella cavolini.

Due to sedentary lifestyle and harsh environmental conditions, gorgonian coral extracts are recognized as a rich source of novel compounds with various biological activities, of interest to the pharmaceutical and cosmetic industries. The presented study aimed to perform chemical screening of organic extracts and semi-purified fractions obtained from the common Adriatic gorgonian, sea fan, Eunicella cavolini (Koch, 1887) and explore its abilities to exert different biological effects in vitro. Qualitative chemical evaluation revealed the presence of several classes of secondary metabolites extended with mass spectrometry analysis and tentative dereplication by using Global Natural Product Social Molecular Networking online platform (GNPS). Furthermore, fractions F4 and F3 showed the highest phenolic (3.28 ± 0.04 mg GAE/g sample) and carotene (23.11 ± 2.48 mg ß-CA/g sample) content, respectively. The fraction F3 inhibited 50% of DPPH (2,2-diphenyl-1-picryl-hydrazyl-hydrate) and ABTS (2,2'-azino-bis (3-ethylbenzthiazolin-6-yl) sulfonic acid) radicals at the concentrations of 767.09 ± 11.57 and 157.16 ± 10.83 µg/mL, respectively. The highest anti-inflammatory potential was exhibited by F2 (IC50 = 198.70 ± 28.77 µg/mL) regarding the inhibition of albumin denaturation and F1 (IC50 = 254.49 ± 49.17 µg/mL) in terms of soybean lipoxygenase inhibition. In addition, the most pronounced antiproliferative effects were observed for all samples (IC50 ranging from 0.82 ± 0.14-231.18 ± 46.13 µg/mL) against several carcinoma cell lines, but also towards non-transformed human fibroblasts pointing to a generally cytotoxic effect. In addition, the antibacterial activity was tested by broth microdilution assay against three human pathogenic bacteria: Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus. The latter was the most affected by fractions F2 and F3. Finally, further purification, isolation and characterization of pure compounds from the most active fractions are under investigation.

In Vitro Confirmation of Siramesine as a Novel Antifungal Agent with In Silico Lead Proposals of Structurally Related Antifungals.

The limited number of medicinal products available to treat of fungal infections makes control of fungal pathogens problematic, especially since the number of fungal resistance incidents increases. Given the high costs and slow development of new antifungal treatment options, repurposing of already known compounds is one of the proposed strategies. The objective of this study was to perform in vitro experimental tests of already identified lead compounds in our previous in silico drug repurposing study, which had been conducted on the known Drugbank database using a seven-step procedure which includes machine learning and molecular docking. This study identifies siramesine as a novel antifungal agent. This novel indication was confirmed through in vitro testing using several yeast species and one mold. The results showed susceptibility of Candida species to siramesine with MIC at concentration 12.5 µg/mL, whereas other candidates had no antifungal activity. Siramesine was also effective against in vitro biofilm formation and already formed biofilm was reduced following 24 h treatment with a MBEC range of 50-62.5 µg/mL. Siramesine is involved in modulation of ergosterol biosynthesis in vitro, which indicates it is a potential target for its antifungal activity. This implicates the possibility of siramesine repurposing, especially since there are already published data about nontoxicity. Following our in vitro results, we provide additional in depth in silico analysis of siramesine and compounds structurally similar to siramesine, providing an extended lead set for further preclinical and clinical investigation, which is needed to clearly define molecular targets and to elucidate its in vivo effectiveness as well.

Search for Novel Lead Inhibitors of Yeast Cytochrome bc1, from Drugbank and COCONUT.

In this work we introduce a novel filtering and molecular modeling pipeline based on a fingerprint and descriptor similarity procedure, coupled with molecular docking and molecular dynamics (MD), to select potential novel quoinone outside inhibitors (QoI) of cytochrome bc1 with the aim of determining the same or different chromophores to usual. The study was carried out using the yeast cytochrome bc1 complex with its docked ligand (stigmatellin), using all the fungicides from FRAC code C3 mode of action, 8617 Drugbank compounds and 401,624 COCONUT compounds. The introduced drug repurposing pipeline consists of compound similarity with C3 fungicides and molecular docking (MD) simulations with final QM/MM binding energy determination, while aiming for potential novel chromophores and perserving at least an amide (R1HN(C=O)R2) or ester functional group of almost all up to date C3 fungicides. 3D descriptors used for a similarity test were based on the 280 most stable Padel descriptors. Hit compounds that passed fingerprint and 3D descriptor similarity condition and had either an amide or an ester group were submitted to docking where they further had to satisfy both Chemscore fitness and specific conformation constraints. This rigorous selection resulted in a very limited number of candidates that were forwarded to MD simulations and QM/MM binding affinity estimations by the ORCA DFT program. In this final step, stringent criteria based on (a) sufficiently high frequency of H-bonds; (b) high interaction energy between protein and ligand through the whole MD trajectory; and (c) high enough QM/MM binding energy scores were applied to further filter candidate inhibitors. This elaborate search pipeline led finaly to four Drugbank synthetic lead compounds (DrugBank) and seven natural (COCONUT database) lead compounds-tentative new inhibitors of cytochrome bc1. These eleven lead compounds were additionally validated through a comparison of MM/PBSA free binding energy for new leads against those obtatined for 19 QoIs.

Combined Machine Learning and Molecular Modelling Workflow for the Recognition of Potentially Novel Fungicides.

Novel machine learning and molecular modelling filtering procedures for drug repurposing have been carried out for the recognition of the novel fungicide targets of Cyp51 and Erg2. Classification and regression approaches on molecular descriptors have been performed using stepwise multilinear regression (FS-MLR), uninformative-variable elimination partial-least square regression, and a non-linear method called Forward Stepwise Limited Correlation Random Forest (FS-LM-RF). Altogether, 112 prediction models from two different approaches have been built for the descriptor recognition of fungicide hit compounds. Aiming at the fungal targets of sterol biosynthesis in membranes, antifungal hit compounds have been selected for docking experiments from the Drugbank database using the Autodock4 molecular docking program. The results were verified by Gold Protein-Ligand Docking Software. The best-docked conformation, for each high-scored ligand considered, was submitted to quantum mechanics/molecular mechanics (QM/MM) gradient optimization with final single point calculations taking into account both the basis set superposition error and thermal corrections (with frequency calculations). Finally, seven Drugbank lead compounds were selected based on their high QM/MM scores for the Cyp51 target, and three were selected for the Erg2 target. These lead compounds could be recommended for further in vitro studies.

Influence of beeswax adulteration with paraffin on the composition and quality of honey determined by physico-chemical analyses, 1H NMR, FTIR-ATR and HS-SPME/GC-MS.

Analytical parameters were determined for the first time in honey produced in the honeycomb constructed on comb foundations adulterated with 90% of paraffin (PF-H) and compared to honey ripened in genuine beeswax (BWF-H) using physico-chemical and spectroscopic techniques (1H NMR, FTIR-ATR, HS-SPME/GC-MS). Water content was significantly higher (SH) and glucose/water ratio significantly lower in PF-H samples. The contents of acetic and citric acid were marginally significantly higher (MSH) in PF-H samples. These findings suggest that adulterated beeswax affects composition of honey as the set of altered parameters indicate chemical changes leaning towards fermentative processes. Moderately changed headspace chemical profile of PF-H honey was determined depending on the floral source (pentanal, α-pinene and benzaldehyde were SH in BWF-H sunflower honey; butanal was MSH, and 2-phenylethanol was more abundant in BWF-H black locust honey). Higher percentage of nonanal, octane and ß-damascenone were found in PF-H samples that could indicate more intensive oxidation.

A comprehensive evaluation of novel oximes in creation of butyrylcholinesterase-based nerve agent bioscavengers.

A well-considered treatment of acute nerve agents poisoning involves the exogenous administration of butyrylcholinesterase (BChE, EC 3.1.1.8) as a stoichiometric bioscavenger efficient in preventing cholinergic crises caused by acetylcholinesterase (AChE, EC 3.1.1.7) inhibition. An additional improvement in medical countermeasures would be to use oximes that could reactivate BChE as well to upgrade bioscavenging from stoichiometric to oxime-assisted catalytic. Therefore, in this paper we investigated the potency of 39 imidazolium and benzimidazolium oximes (36 compounds synthesized for the first time) to be considered as the reactivators specifically designed for reactivation of phosphylated human BChE. Their efficiency in the reactivation of paraoxon-, VX-, and tabun-inhibited human BChE, as well as human AChE was tested and compared with the efficiencies of HI-6 and obidoxime, used in medical practice today. A comprehensive analysis was performed for the most promising oximes defining kinetic parameters of reactivation as well as interactions with uninhibited BChE. Furthermore, experimental data were compared with computational studies (docking, QSAR analysis) as a starting point in future oxime structure refinement. Considering the strict criteria set for in vivo applications, we determined the cytotoxicity of lead oximes on two cell lines. Among the tested oxime library, one imidazolium compound was selected for preliminary in vivo antidotal study in mice. The obtained protection in VX poisoning outlines its potential in development oxime-assisted OP-bioscavenging with BChE.

Durbin-Watson partial least-squares regression applied to MIR data on adulteration with edible oils of different origins.

A novel method for quantitative prediction and variable-selection on spectroscopic data, called Durbin-Watson partial least-squares regression (dwPLS), is proposed in this paper. The idea is to inspect serial correlation in infrared data that is known to consist of highly correlated neighbouring variables. The method selects only those variables whose intervals have a lower Durbin-Watson statistic (dw) than a certain optimal cutoff. For each interval, dw is calculated on a vector of regression coefficients. Adulteration of cold-pressed linseed oil (L), a well-known nutrient beneficial to health, is studied in this work by its being mixed with cheaper oils: rapeseed oil (R), sesame oil (Se) and sunflower oil (Su). The samples for each botanical origin of oil vary with respect to producer, content and geographic origin. The results obtained indicate that MIR-ATR, combined with dwPLS could be implemented to quantitative determination of edible-oil adulteration.

Spectroscopic and Chemometric Analysis of Binary and Ternary Edible Oil Mixtures: Qualitative and Quantitative Study.

The aim of this study was to investigate the feasibility of FTIR-ATR spectroscopy coupled with the multivariate numerical methodology for qualitative and quantitative analysis of binary and ternary edible oil mixtures. Four pure oils (extra virgin olive oil, high oleic sunflower oil, rapeseed oil, and sunflower oil), as well as their 54 binary and 108 ternary mixtures, were analyzed using FTIR-ATR spectroscopy in combination with principal component and discriminant analysis, partial least-squares, and principal component regression. It was found that the composition of all 166 samples can be excellently represented using only the first three principal components describing 98.29% of total variance in the selected spectral range (3035-2989, 1170-1140, 1120-1100, 1093-1047, and 930-890 cm(-1)). Factor scores in 3D space spanned by these three principal components form a tetrahedral-like arrangement: pure oils being at the vertices, binary mixtures at the edges, and ternary mixtures on the faces of a tetrahedron. To confirm the validity of results, we applied several cross-validation methods. Quantitative analysis was performed by minimization of root-mean-square error of cross-validation values regarding the spectral range, derivative order, and choice of method (partial least-squares or principal component regression), which resulted in excellent predictions for test sets (R(2) > 0.99 in all cases). Additionally, experimentally more demanding gas chromatography analysis of fatty acid content was carried out for all specimens, confirming the results obtained by FTIR-ATR coupled with principal component analysis. However, FTIR-ATR provided a considerably better model for prediction of mixture composition than gas chromatography, especially for high oleic sunflower oil.

Interval ridge regression (iRR) as a fast and robust method for quantitative prediction and variable selection applied to edible oil adulteration.

A novel quantitative prediction and variable selection method called interval ridge regression (iRR) is studied in this work. The method is performed on six data sets of FTIR, two data sets of UV-vis and one data set of DSC. The obtained results show that models built with ridge regression on optimal variables selected with iRR significantly outperfom models built with ridge regression on all variables in both calibration (6 out of 9 cases) and validation (2 out of 9 cases). In this study, iRR is also compared with interval partial least squares regression (iPLS). iRR outperfomed iPLS in validation (insignificantly in 6 out of 9 cases and significantly in one out of 9 cases for p<0.05). Also, iRR can be a fast alternative to iPLS, especially in case of unknown degree of complexity of analyzed system, i.e. if upper limit of number of latent variables is not easily estimated for iPLS. Adulteration of hempseed (H) oil, a well known health beneficial nutrient, is studied in this work by mixing it with cheap and widely used oils such as soybean (So) oil, rapeseed (R) oil and sunflower (Su) oil. Binary mixture sets of hempseed oil with these three oils (HSo, HR and HSu) and a ternary mixture set of H oil, R oil and Su oil (HRSu) were considered. The obtained accuracy indicates that using iRR on FTIR and UV-vis data, each particular oil can be very successfully quantified (in all 8 cases RMSEP<1.2%). This means that FTIR-ATR coupled with iRR can very rapidly and effectively determine the level of adulteration in the adulterated hempseed oil (R(2)>0.99).

Structure-property relationship of quinuclidinium surfactants--Towards multifunctional biologically active molecules.

Motivated by diverse biological and pharmacological activity of quinuclidine and oxime compounds we have synthesized and characterized novel class of surfactants, 3-hydroxyimino quinuclidinium bromides with different alkyl chains lengths (CnQNOH; n=12, 14 and 16). The incorporation of non conventional hydroxyimino quinuclidinium headgroup and variation in alkyl chain length affects hydrophilic-hydrophobic balance of surfactant molecule and thereby physicochemical properties important for its application. Therefore, newly synthesized surfactants were characterized by the combination of different experimental techniques: X-ray analysis, potentiometry, electrical conductivity, surface tension and dynamic light scattering measurements, as well as antimicrobial susceptibility tests. Comprehensive investigation of CnQNOH surfactants enabled insight into structure-property relationship i.e., way in which the arrangement of surfactant molecules in the crystal phase correlates with their solution behavior and biologically activity. The synthesized CnQNOH surfactants exhibited high adsorption efficiency and relatively low critical micelle concentrations. In addition, all investigated compounds showed very potent and promising activity against Gram-positive and clinically relevant Gram-negative bacterial strains compared to conventional antimicrobial agents: tetracycline and gentamicin. The overall results indicate that bicyclic headgroup with oxime moiety, which affects both hydrophilicity and hydrophobicity of CnQNOH molecule in addition to enabling hydrogen bonding, has dominant effect on crystal packing and physicochemical properties. The unique structural features of cationic surfactants with hydroxyimino quinuclidine headgroup along with diverse biological activity have made them promising structures in novel drug discovery. Obtained fundamental understanding how combination of different functionalities in a single surfactant molecule affects its physicochemical properties represents a good starting point for further biological research.