Multivariate assessment of lipophilicity scales-computational and reversed phase thin-layer chromatographic indices

Multiobjective optimization of effect-directed planar chromatography as a promising tool for fast selection of polypotent natural products

A new method for efficiently selecting polypotent natural products is proposed in this study. The method involves using effect-directed HPTLC data and multiobjective optimization algorithms to extract chromatographic signals from HPTLC bioassay images. Three different multiobjective optimization methods, namely Derringer's desirability approach, Technique for order of preference by similarity to ideal solution (TOPSIS), and Sum of ranking differences (SRD), were applied to the chromatographic signals. In combination with jackknife cross-validation, Derringer's approach and TOPSIS demonstrated high similarity in finding the best (most polypotent), next to the best, next to the worst, and worst (least polypotent) extracts, while the SRD resulted in slightly different outcomes. Furthermore, a new method for identifying the chromatographic features that characterize the most polypotent extracts was proposed. This method is based on partial least square regression (PLS) and can be used in combination with HPTLC-chemical fingerprints to predict the desirability of new extracts. The resulting PLS models demonstrated high statistical performance with determination coefficients ranging from R2 = 0.885 in the case of Derringer's desirability, to 0.986 for TOPSIS. However, the PLS modeling of SRD values was not successful.

Assessment of Lipophilicity Parameters of Antimicrobial and Immunosuppressive Compounds

Lipophilicity in addition to the solubility, acid-base character and stability is one of the most important physicochemical parameters of a compound required to assess the ADMET properties (absorption, distribution, metabolism, excretion and toxicity) of a bioactive molecule. Therefore, the subject of this work was to determine the lipophilicity parameters of selected antimicrobial and immunosuppressive compounds such as delafloxacin, linezolid, sutezolid, ceftazidime, everolimus and zotarolimus using thin-layer chromatography in reversed phase system (RP-TLC). The chromatographic parameters of lipophilicity (R_MW) for tested compounds were determined on different stationary phases: RP18F₂₅₄, RP18WF₂₅₄ and RP2F₂₅₄ using ethanol, acetonitrile, and propan-2-ol as organic modifiers of mobile phases used. Chromatographically established R_MW values were compared with partition coefficients obtained by different computational methods (AlogPs, AClogP, AlogP, MlogP, XlogP2, XlogP3, logP_KOWWIN, ACD/logP, milogP). Both cluster and principal component analysis (CA and PCA) of the received results allowed us to compare the lipophilic nature of the studied compounds. The sum of ranking differences analysis (SRD) of all lipophilicity parameters was helpful to select the most effective method of determining the lipophilicity of the investigated compounds. The presented results demonstrate that RP-TLC method may be a good tool in determining the lipophilic properties of studied substances. Obtained lipophilic parameters of the compounds can be valuable in the design of their new derivatives as efficient antimicrobial and immunosuppressive agents.

Comparative Study of the Lipophilicity of Selected Anti-Androgenic and Blood Uric Acid Lowering Compounds

This study aimed to evaluate the lipophilicity of a series substances lowering the concentration of uric acid in blood and anti-androgen drugs by thin-layer chromatography in reversed-phase systems (RP-TLC, RP-HPTLC) and computational methods. The chromatographic parameter of lipophilicity (R_MW) of tested compounds was determined on three stationary phases, i.e., RP18F₂₅₄, RP18WF₂₅₄ and RP2F₂₅₄, using ethanol-water, propan-2-ol-water and acetonitrile-water in various volume compositions as mobile phases. The chromatographic analysis led to determining the experimental value of the lipophilicity parameter for each of the tested compounds, including those for which the experimental value of the partition coefficient (logP_exp) as a measure of lipophilicity is not well described in available databases, such as febuxostat, oxypurinol, ailanthone, abiraterone and teriflunomide. The chromatographic parameters of lipophilicity were compared with the logP values obtained with various software packages, such as AClogP, AlogPs, AlogP, MlogP, XlogP2, XlogP3, ACD/logP and logP_KOWWIN. The obtained results indicate that, among selected chromatographic parameters of lipophilicity, both experimental and calculated logP values gave similar results, and these RP-TLC or RP-HPTLC systems can be successfully applied to estimate the lipophilicity of studied heterocyclic compounds belonging to two different pharmacological groups. This work also illustrates the similarity and difference existing between the tested compounds under study using the chemometric methods, such as principal component analysis (PCA) and cluster analysis (CA). In addition, a relatively new approach based on the sum of ranking differences (SRD) was used to compare the chromatographically obtained and theoretical lipophilicity descriptors of studied compounds.

Comprehensive examination and comparison of machine learning techniques for the quantitative determination of adulterants in honey using Fourier infrared spectroscopy with attenuated total reflectance accessory

Facile, robust, and accurate analyses of honey adulterants are required in the honey industry to assess its purity for commercialization purposes. A stacked regression ensemble approach using Fourier transform infrared spectroscopic method was developed for the quantitative determination of corn, cane, beet, and rice syrup adulterants in honey. A training set (n=81) was used to predict the percent adulterant composition of the aforementioned constituents in an independent test set (n=32). A comprehensive comparison of the performance of various machine learning techniques including support vector regression using linear function, least absolute shrinkage and selection operator, ride regression, elastic net, partial least squares, random forests, recursive partitioning and regression trees, gradient boosting, and gaussian process regression was assessed. The predictive performance of the aforementioned machine learning approaches was then compared with stacked regression, an ensemble learning technique which collates the performance of the various abovementioned techniques. Results show that stacked regression did not primarily outperform other techniques across all four syrup adulterant constituents in the testing set data. Further, elastic net generalized linear model generated the optimum results (Rootmeansquareerrorofprediction(RMSEP)_average=0.0107,R_average²=0.809) across all four honey adulterant constituents. Elastic net coupled with Fourier transform infrared spectroscopy may offer a novel, direct, and accurate method of simultaneously quantifying corn, cane, beet, and rice syrup adulterants in honey.

Multiobject Optimization of National Football League Drafts: Comparison of Teams and Experts

Predicting the success of National Football League drafts has always been an exciting issue for the teams, fans and even for scientists. Among the numerous approaches, one of the best techniques is to ask the opinion of sport experts, who have the knowledge and past experiences to rate the drafts of the teams. When asking a set of sport experts to evaluate the performances of teams, a multicriteria decision making problem arises unavoidably. The current paper uses the draft evaluations of the 32 NFL teams given by 18 experts: a novel multicriteria decision making tool has been applied: the sum of ranking differences (SRD). We introduce a quick and easy-to-follow approach on how to evaluate the performance of the teams and the experts at the same time. Our results on the 2021 NFL draft data indicate that Green Bay Packers has the most promising drafts for 2021, while the experts have been grouped into three distinct groups based on the distance to the hypothetical best evaluation. Even the coding options can be tailored according to the experts’ opinions. Statistically correct (pairwise or group) comparisons can be made using analysis of variance (ANOVA). A comparison to TOPSIS ranking revealed that SRD gives a more objective ranking due to the lack of predefined weights.

Comprehensible Visualization of Multidimensional Data: Sum of Ranking Differences-Based Parallel Coordinates

A novel visualization technique is proposed for the sum of ranking differences method (SRD) based on parallel coordinates. An axis is defined for each variable, on which the data are depicted row-wise. By connecting data, the lines may intersect. The fewer intersections between the variables, the more similar they are and the clearer the figure becomes. Therefore, the visualization depends on what techniques are used to order the variables. The key idea is to employ the SRD method to measure the degree of similarity of the variables, establishing a distance-based order. The distances between the axes are not uniformly distributed in the proposed visualization; their closeness reflects similarity, according to their SRD value. The proposed algorithm identifies false similarities through an iterative approach, where the angles between the SRD values determine which side a variable is plotted. Visualization of the algorithm is provided by MATLAB/Octave source codes. The proposed tool is applied to study how the sources of greenhouse gas emissions can be grouped based on the statistical data of the countries. A comparison to multidimensional scaling (MDS)-based ordering is also given. The use case demonstrates the applicability of the method and the synergies of the incorporation of the SRD method into parallel coordinates.

Multivariate assessment of anticancer oleanane triterpenoids lipophilicity

Naturally occurring molecules are excellent sources of lead compounds. A series of oleanolic acid (OA) derivatives previously synthesized in our laboratory, which show promising antitumor activity, have been analyzed in terms of lipophilicity evaluation applying chromatographic and computational approaches. Retention data obtained on three reversed-phase liquid chromatography stationary phases (RP-HPLC) and immobilized artificial membrane chromatography (IAM-HPLC) were compared with computational methods using chemometric tools such as cluster analysis, principal component analysis and sum of ranking differences. To investigate the molecular mechanism of retention quantitive structure retention relationship analysis was performed, based on the genetic algorithm coupled with multiple linear regression (GA-MLR). The obtained results suggested that the ionization potential of studied molecules significantly affects their retention in classical RP-HPLC. In IAM-HPLC additionally, polarizability-related descriptors also play an essential role in that process. The lipophilicity indices comparison shows significant differences between the computational lipophilicity and chromatographically determined ones.

RP-18 TLC retention data and calculated physico-chemical parameters as predictors of soil-water partition and bioconcentration of organic sunscreens

RP-18 TLC chromatography was used to evaluate the impact on the environment (mobility in soil expressed as soil-water partition coefficient, log K_oc; bioconcentration factor in aquatic organisms, log BCF) of several cosmetic raw materials - sunscreens, preservatives and vitamins. The retention parameters R_M⁰ (R_M extrapolated to zero concentration of an organic modifier in a mobile phase), S (slope), PC₁ (1st principal component) and R_M^75% (single TLC run parameter for mobile phases containing 75% (v/v) of an organic modifier) obtained for six organic modifiers (methanol, acetonitrile, THF, acetone, dioxane, DMF) were used as the sole descriptors or combined with calculated physico-chemical properties (PSA - polar surface area; M_W - molecular weight; V_M - molar volume) of studied compounds. The chromatographic parameters considered in this study are, generally speaking, good predictors of the compounds' mobility in soil or the affinity for aquatic organisms. The parameters R_M⁰, S and R_M^75% obtained for THF, dioxane and acetone may be used to investigate even very lipophilic compounds. R_M^75% is of a little bit limited use but it should be considered a time- and cost-effective alternative to the chromatographic parameters obtained by extrapolation or interpolation methods. In the case of some environmental parameters investigated in this study additional descriptors (PSA) have a significant influence on the quality of correlations.

Estimation of the lipophilicity of purine-2,6-dione-based TRPA1 antagonists and PDE4/7 inhibitors with analgesic activity

Lipophilicity is one of the principal QSAR parameters which influences among others the pharmacodynamics and pharmacokinetic properties of a drug candidates. In this paper, the lipophilicity of 14 amide derivatives of 1,3-dimethyl-2,6-dioxopurin-7-yl-alkylcarboxylic acids as multifunctional TRPA1 channel antagonists and phosphodiesterase 4/7 inhibitors with analgesic activity were investigated, using reversed-phase thin-layer chromatography method. It was observed that the retention behavior of the analyzed compounds was dependent on their structural features i.e. an aliphatic linker length, a kind of substituent at 8 position of purine-2,6-dione scaffold as well as on a substitution in a phenyl group. The experimental parameters (R_M0) were compared with computationally calculated partition coefficient values by Principal Component Analysis (PCA). To verify the influence of lipophilic parameter of the investigated compounds on their biological activity the Kruskal-Wallis test was performed. The lowest lipophilicity was observed for the compounds with weak PDE4/7 inhibitory potency. The differences between the lipophilicity of potent inhibitors and inactive compounds were statistically significant. It was found that the presence of more lipophilic propoxy- or butoxy- substituents as well as the elongation of the aliphatic chain to propylene one between the purine-2,6-dione core and amide group were preferable for desired multifunctional activity.

Evaluation of drug-likeness and ADME properties of sunscreens and preservatives using reversed-phase thin layer chromatographic retention data and calculated descriptors

RP-18 TLC chromatography was used to evaluate the pharmacokinetic properties (volume of distribution, V_D; plasma protein binding, %PPB; the ability to cross the blood-brain barrier expressed as log PS and log BB) of several cosmetic raw materials - sunscreen and preservatives. The majority of these compounds are intended for topical use on skin and their drug-likeness and the ability to cross biological barriers are undesired. The retention parameters R_M⁰, S, PC₁ and R_M^{75 %} obtained for mobile phases containing six organic modifiers (methanol, acetonitrile, THF, acetone, dioxane, DMF) were used as the sole descriptors or combined with calculated physicochemical properties (PSA, M_W, V_M) of studied compounds. The chromatographic parameters considered in this study are, generally speaking, good predictors of the compounds' pharmacokinetic properties V_D, %PPB and log PS. R_M^{75 %} and the novel parameters derived from it (R_M^{75 %}/M_W and R_M^{75 %}/V_M) can be considered time- and cost-effective alternatives to the chromatographic parameters obtained by extrapolation or interpolation methods. In the case of some pharmacokinetic properties investigated in this study additional descriptors (PSA) have a significant influence on the quality of correlations.

Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 1: Theory and characteristics†

Quantification of the similarity of objects is a key concept in many areas of computational science. This includes cheminformatics, where molecular similarity is usually quantified based on binary fingerprints. While there is a wide selection of available molecular representations and similarity metrics, there were no previous efforts to extend the computational framework of similarity calculations to the simultaneous comparison of more than two objects (molecules) at the same time. The present study bridges this gap, by introducing a straightforward computational framework for comparing multiple objects at the same time and providing extended formulas for as many similarity metrics as possible. In the binary case (i.e. when comparing two molecules pairwise) these are naturally reduced to their well-known formulas. We provide a detailed analysis on the effects of various parameters on the similarity values calculated by the extended formulas. The extended similarity indices are entirely general and do not depend on the fingerprints used. Two types of variance analysis (ANOVA) help to understand the main features of the indices: (i) ANOVA of mean similarity indices; (ii) ANOVA of sum of ranking differences (SRD). Practical aspects and applications of the extended similarity indices are detailed in the accompanying paper: Miranda-Quintana et al. J Cheminform. 2021. https://doi.org/10.1186/s13321-021-00504-4 . Python code for calculating the extended similarity metrics is freely available at: https://github.com/ramirandaq/MultipleComparisons .

Predicting Pharmacokinetic Properties of Potential Anticancer Agents via Their Chromatographic Behavior on Different Reversed Phase Materials

The Quantitative Structure-Activity Relationship (QSAR) methodology was used to predict biological properties, i.e., the blood–brain distribution (log BB), fraction unbounded in the brain (f_u,brain), water-skin permeation (log K_p), binding to human plasma proteins (log K_a,HSA), and intestinal permeability (Caco-2), for three classes of fused azaisocytosine-containing congeners that were considered and tested as promising drug candidates. The compounds were characterized by lipophilic, structural, and electronic descriptors, i.e., chromatographic retention, topological polar surface area, polarizability, and molecular weight. Different reversed-phase liquid chromatography techniques were used to determine the chromatographic lipophilicity of the compounds that were tested, i.e., micellar liquid chromatography (MLC) with the ODS-2 column and polyoxyethylene lauryl ether (Brij 35) as the effluent component, an immobilized artificial membrane (IAM) chromatography with phosphatidylcholine column (IAM.PC.DD2) and chromatography with end-capped octadecylsilyl (ODS) column using aqueous solutions of acetonitrile as the mobile phases. Using multiple linear regression, we derived the statistically significant quantitative structure-activity relationships. All these QSAR equations were validated and were found to be very good. The investigations highlight the significance and possibilities of liquid chromatographic techniques with three different reversed-phase materials and QSARs methods in predicting the pharmacokinetic properties of our important organic compounds and reducing unethical animal testing.

Monitoring of soluble pectin content in orange juice by means of MIR and TD-NMR spectroscopy combined with machine learning

This study represents a rapid and non-destructive approach based on mid-infrared (MIR) spectroscopy, time domain nuclear magnetic resonance (TD-NMR), and machine learning classification models (ML) for monitoring soluble pectin content (SPC) changes in orange juice. Current reference methods of SPC in orange juice are laborious, requiring several extractions with successive adjustments hindering rapid process intervention. 109 fresh orange juices samples, representing different harvests, were analysed using MIR, TD-NMR and reference method. Unsupervised algorithms were applied for natural clustering of MIR and TD-NMR data in two groups. Analyses of variance of the two MIR and TD-NMR datasets show that only the MIR groups were different at 95% confidence for SPC average values. This approach allows build classification models based on MIR data achieving 85% and 89% of accuracy. Results demonstrate that MIR/ML can be a suitable strategy for the quick assessment of SPC trends in orange juices.

Comparative chemometric and quantitative structure-retention relationship analysis of anisotropic lipophilicity of 1-arylsuccinimide derivatives determined in high-performance thin-layer chromatography system with aprotic solvents

Numerous structurally different amides and imides including succinimide derivatives exhibit diverse bioactive potential. The development of new compounds requires rationalization in the design in order to provide structural changes that guarantee favorable physico-chemical properties, pharmacological activity and safety. In the present research, a comprehensive study with comparison of the chromatographic lipophilicity and other physico-chemical properties of five groups of 1-arylsuccinimide derivatives was conducted. The chemometric analysis of their physico-chemical properties was carried out by using unsupervised (hierarchical cluster analysis and principal component analysis) and supervised pattern recognition methods (linear discriminant analysis), while the correlations between the in silico molecular features and chromatographic lipophilicity were examined applying linear and non-linear Quantitative Structure-Retention Relationship (QSRR) approaches. The main aim of the conducted research was to determine similarities and dissimilarities among the studied 1-arylsuccinimides, to point out the molecular features which have significant influence on their lipophilicity, as well as to establish high-quality QSRR models which can be used in prediction of chromatographic lipophilicity of structurally similar 1-arylsuccinimides. This study is a continuation of analysis and determination of the physico-chemical properties of 1-arylsuccinimides which could be important guidelines in further in vitro and eventually in vivo studies of their biological potential.

Quantitative structure-chromatographic retention relationship of synthesized peptides (HGRFG, NPNPT) and their derivatives

Carapax Trionycis extract peptides (HGRFG, NPNPT) are able to protect against CCl₄-induced liver fibrosis. Therefore, this study applies to deal with chromatographic lipophilicity determination of synthesized peptides (HGRFG, NPNPT) and their derivatives using reversed-phase high performance liquid chromatography (RP-HPLC) combined with methanol-water mobile phase and two reversed-phase chromatographic columns (COSMOISL 5C18-MS-II and SHIMADZU-C18). The chromatographic lipophilicity of the analyzed compounds was expressed as logk_w constant and correlated with lipophilicity descriptors. Quantitative structure-retention relationships (QSRR) analysis was performed to imitate chromatographic lipophilicity behavior using molecular descriptors. Modeling was performed using linear regression (LR) and multiple linear regression (MLR) methods with the help of principal component analysis (PCA) and hierarchical cluster analysis (HCA). The most influential molecular descriptors were lipophilicity descriptors, which are important for molecules ability to pass through biological membranes. All established QSRR models were statistically validated by standards, cross- and external validation parameters. According to these statistical validation parameters, MLR models (R² > 0.856) were better for chromatographic lipophilicity prediction of peptide compounds. It can be concluded that chromatographic systems with COSMOISL 5C18-MS-II column were better for modeling of logk_w than systems with SHIMADZU-C18 column. Modeling was performed in order to obtain lipophilicity profiles of investigated compounds as future drug candidates.

Chromatographic and computational screening of anisotropic lipophilicity and pharmacokinetics of newly synthesized 1-aryl-3-ethyl-3-methylsuccinimides

The present study is focused on a series of newly synthesized 1-aryl-3-ethyl-3-methylsuccinimide derivatives, as potential anticonvulsants. The retention behavior of eleven succinimide derivatives was determined by using reversed phase high performance liquid chromatography (RP-HPLC) and reversed phase high performance thin layer chromatography (RP-HPTLC). The estimated retention behavior was correlated with partition (logP) and distribution coefficients (logD). These high correlations pointed out that the determined retention parameters (logk₀ and R_M⁰) can be considered chromatographic (anisotropic) lipophilicity of the studied succinimide derivatives. The structural properties, which dominantly affect the chromatographic lipophilicity, were determined as well. The significant correlations between the chromatographic lipophilicity and plasma protein binding (PPB), Madin-Darby Canine Kidney (MDCK) cells permeability, volume of distribution (Vd) and absorption constant (Ka) indicate the strong influence of lipophilicity on pharmacokinetics of 1-aryl-3-ethyl-3-methylsuccinimide derivatives. These derivatives have also been tested applying Comprehensive Medicinal Chemistry (CMC) drug-like rules which confirmed their drug-like properties. Besides, their blood-brain penetration (BBB) ability has been estimated applying the set of Clark's rules and by using Pre-ADMET software. Regarding toxicity, it was predicted that only one compound from the set might have toxic effects by blocking the hERG potassium channel. The present study reveals which molecular features in the structure of novel succinimide derivatives could be crucial for their lipophilicity, and consequently for their pharmacokinetic properties. The results indicate that the newly synthesized series of succinimide derivatives should be further considered in design of novel anticonvulsants.

Lipophilicity Determination of Antifungal Isoxazolo[3,4-b]pyridin-3(1H)-ones and Their N1-Substituted Derivatives with Chromatographic and Computational Methods

The lipophilicity of a molecule is a well-recognized as a crucial physicochemical factor that conditions the biological activity of a drug candidate. This study was aimed to evaluate the lipophilicity of isoxazolo[3,4-b]pyridine-3(1H)-ones and their N1-substituted derivatives, which demonstrated pronounced antifungal activities. Several methods, including reversed-phase thin layer chromatography (RP-TLC), reversed phase high-performance liquid chromatography (RP-HPLC), and micellar electrokinetic chromatography (MEKC), were employed. Furthermore, the calculated logP values were estimated using various freely and commercially available software packages and online platforms, as well as density functional theory computations (DFT). Similarities and dissimilarities between the determined lipophilicity indices were assessed using several chemometric approaches. Principal component analysis (PCA) indicated that other features beside lipophilicity affect antifungal activities of the investigated derivatives. Quantitative-structure-retention-relationship (QSRR) analysis by means of genetic algorithm-partial least squares (GA-PLS)-was implemented to rationalize the link between the physicochemical descriptors and lipophilicity. Among the studied compounds, structure 16 should be considered as the best starting structure for further studies, since it demonstrated the lowest lipophilic character within the series while retaining biological activity. Sum of ranking differences (SRD) analysis indicated that the chromatographic approach, regardless of the technique employed, should be considered as the best approach for lipophilicity assessment of isoxazolones.

Lipophilicity Determination of Quaternary (Fluoro)Quinolones by Chromatographic and Theoretical Approaches

Lipophilicity is a vital physicochemical parameter of a molecule, which affects several biological processes such as absorption, tissue distribution, and pharmacokinetic properties. In this study, evaluation of lipophilicities of a series of novel fluoroquinolone-Safirinium dye hybrids using chromatographic and computational methods is presented. Fluoroquinolone-Safirinium dye hybrids have been synthesized as new dual-acting hydrophilic antibacterial agents. Reversed phase thin-layer chromatography and micellar electrokinetic chromatography experiments were carried out. Furthermore, logP values of the target structures were predicted by means of different software platforms and algorithms. In order to assess similarities and dissimilarities of the obtained lipophilicity indexes, cluster analysis and sum of ranking differences were performed. The significant differences of calculated logP values (α = 0.05, p < 0.001) indicated that an experimental approach is necessary for lipophilicity prediction of this class of antibiotics. Chromatographic data indicated that the newly synthesized hybrid (fluoro)quinolone-based quaternary ammonium derivatives show less lipophilic character than the parent (fluoro)quinolones. Additionally, the chromatographically obtained lipophilicity indexes were evaluated for possible application in quantitative retention-activity relationships. The established lipophilicity models have the potential to predict antimicrobial activities of a series of quaternary (fluoro)quinolones against Bacillus subtilis, Escherichia coli, and Proteus vulgaris.

Comparison of the Utility of RP-TLC Technique and Different Computational Methods to Assess the Lipophilicity of Selected Antiparasitic, Antihypertensive, and Anti-inflammatory Drugs

The aim of this study was to assess the lipophilicity of selected antiparasitic, antihypertensive and non-steroidal anti-inflammatory drugs (NSAIDs) by means of reversed phase-thin layer chromatography (RP-TLC) as well by using Soczewiński-Wachtmeister's and J. Ościk's equations. The lipophilicity parameters of all examined compounds obtained under various chromatographic systems (i.e., methanol-water and acetone-water, respectively) and those determined on the basis of Soczewiński-Wachtmeister's and Ościk's equations (i.e., R_MWS and R_MWO) were compared with the theoretical ones (e.g., AlogPs, AClogP, milogP, AlogP, MlogP, XlogP2, XlogP3) and the experimental value of the partition coefficient (logP_exp). It was found that the R_MWS parameter may be a good alternative tool in describing the lipophilic nature of biologically active compounds with a high and low lipophilicity (i.e., antihypertensive and antiparasitic drugs). Meanwhile, the R_MWO was more suitable for compounds with a medium lipophilicity (i.e., non-steroidal anti-inflammatory drugs). The chromatographic parameter _0(a) can be helpful for the prediction of partition coefficients, i.e., AClogP, XlogP3, as well as logP_exp of examined compounds.

Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics

Machine learning classification algorithms are widely used for the prediction and classification of the different properties of molecules such as toxicity or biological activity. the prediction of toxic vs. non-toxic molecules is important due to testing on living animals, which has ethical and cost drawbacks as well. The quality of classification models can be determined with several performance parameters. which often give conflicting results. In this study, we performed a multi-level comparison with the use of different performance metrics and machine learning classification methods. Well-established and standardized protocols for the machine learning tasks were used in each case. The comparison was applied to three datasets (acute and aquatic toxicities) and the robust, yet sensitive, sum of ranking differences (SRD) and analysis of variance (ANOVA) were applied for evaluation. The effect of dataset composition (balanced vs. imbalanced) and 2-class vs. multiclass classification scenarios was also studied. Most of the performance metrics are sensitive to dataset composition, especially in 2-class classification problems. The optimal machine learning algorithm also depends significantly on the composition of the dataset.

Intercorrelation Limits in Molecular Descriptor Preselection for QSAR/QSPR

QSAR/QSPR (quantitative structure-activity/property relationship) modeling has been a prevalent approach in various, overlapping sub-fields of computational, medicinal and environmental chemistry for decades. The generation and selection of molecular descriptors is an essential part of this process. In typical QSAR workflows, the starting pool of molecular descriptors is rationalized based on filtering out descriptors which are (i) constant throughout the whole dataset, or (ii) very strongly correlated to another descriptor. While the former is fairly straightforward, the latter involves a level of subjectivity when deciding what exactly is considered to be a strong correlation. Despite that, most QSAR modeling studies do not report on this step. In this study, we examine in detail the effect of various possible descriptor intercorrelation limits on the resulting QSAR models. Statistical comparisons are carried out based on four case studies from contemporary QSAR literature, using a combined methodology based on sum of ranking differences (SRD) and analysis of variance (ANOVA).

Influence of pH and organic modifiers on the dissociation constants of selected drugs using reversed-phase thin-layer chromatography: Comparison with other techniques and computational tools

Knowledge of the acid-base dissociation constants of drugs is the key to understanding their biopharmaceutical characteristics. In the present work, the effect of pH and organic modifiers (acetonitrile and methanol) was investigated in the determination of dissociation constants (pK_a ) of nine representative drugs (atenolol, betahistine, clarithromycin, deferiprone, diclofenac, ibuprofen, metoprolol, naproxen and propranolol) using reversed-phase thin-layer chromatography. Mobile phase consisting of various buffers and methanol-acetonitrile (10, 20, 30, 40, 50 and 60%, v/v) was used to evaluate the retention pattern on reversed-phase plates. Compared with methanol, acetonitrile gave better results for the experimentally determined pK_a values by extrapolation to zero organic modifier volume fractions. To assess the effectiveness of the developed method the results were correlated using principal component analysis and hierarchical cluster analysis. The calculated values of the aqueous dissociation constant were compared with those reported previously using potentiometry and capillary electrophoresis and also with different computational platforms like ACD/Lab, ChemAxon and Jchem calculator. The results obtained by the RPTLC method were in good agreement with potentiometric methods for pK_a determination.

Life beyond the Tanimoto coefficient: similarity measures for interaction fingerprints

Background

Interaction fingerprints (IFP) have been repeatedly shown to be valuable tools in virtual screening to identify novel hit compounds that can subsequently be optimized to drug candidates. As a complementary method to ligand docking, IFPs can be applied to quantify the similarity of predicted binding poses to a reference binding pose. For this purpose, a large number of similarity metrics can be applied, and various parameters of the IFPs themselves can be customized. In a large-scale comparison, we have assessed the effect of similarity metrics and IFP configurations to a number of virtual screening scenarios with ten different protein targets and thousands of molecules. Particularly, the effect of considering general interaction definitions (such as Any Contact, Backbone Interaction and Sidechain Interaction), the effect of filtering methods and the different groups of similarity metrics were studied.

Results

The performances were primarily compared based on AUC values, but we have also used the original similarity data for the comparison of similarity metrics with several statistical tests and the novel, robust sum of ranking differences (SRD) algorithm. With SRD, we can evaluate the consistency (or concordance) of the various similarity metrics to an ideal reference metric, which is provided by data fusion from the existing metrics. Different aspects of IFP configurations and similarity metrics were examined based on SRD values with analysis of variance (ANOVA) tests.

Conclusion

A general approach is provided that can be applied for the reliable interpretation and usage of similarity measures with interaction fingerprints. Metrics that are viable alternatives to the commonly used Tanimoto coefficient were identified based on a comparison with an ideal reference metric (consensus). A careful selection of the applied bits (interaction definitions) and IFP filtering rules can improve the results of virtual screening (in terms of their agreement with the consensus metric). The open-source Python package FPKit was introduced for the similarity calculations and IFP filtering; it is available at: https://github.com/davidbajusz/fpkit.

Electronic supplementary material

The online version of this article (10.1186/s13321-018-0302-y) contains supplementary material, which is available to authorized users.

Ranking and similarity of conventional, microwave and ultrasound element sequential extraction methods

This study aims to compare three extraction techniques of four sequential element extraction steps from soil and sediment samples that were taken from the location of the Pančevo petrochemical industry (Serbia). Elements were extracted using three different techniques: conventional, microwave and ultrasound extraction. A novel procedure - sum of the ranking differences (SRD) - was able to rank the techniques and elements, to see whether this method is a suitable tool to reveal the similarities and dissimilarities in element extraction techniques, provided that a proper ranking reference is available. The concentrations of the following elements Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Si, Sn, Sr, V and Zn were determined through ICP OES. The different efficiencies and recovery values of element concentrations using each of the three extraction techniques were examined by the CRM BCR-701. By using SRD, we obtained a better separation between the different extraction techniques and steps when we rank their differences among the samples while lower separation was obtained according to analysed elements. Appling this method for ordering the elements could be useful for three purposes: (i) to find possible associations among the elements; (ii) to find possible elements that have outlier concentrations or (iii) detect differences in geochemical origin or behaviour of elements. Cross-validation of the SRD values in combination with cluster and principal component analysis revealed the same groups of extraction steps and techniques.

Lipophilicity evaluation of some thiazolyl-1,3,4-oxadiazole derivatives with antifungal activity

The chromatographic behavior of a series of thiazolyl-1,3,4-oxadiazoles with antifungal activity was studied by reverse-phase thin-layer chromatography (RP-TLC). The lipophilicity parameters derived from RP-TLC were correlated with the data derived from liquid-chromatography mass-spectrometry. Good linear relationships were observed between the chromatographic lipophilicity parameters and the theoretical lipophilicity descriptors (logP) generated by various computer software and internet modules. Principal component analysis, applied on the experimental chromatographic lipophilicity indices and the theoretically calculated logP, enabled us to obtain a lipophilicity chart for better vizualization of the similarities and differences of the investigated compounds, which were grouped by k-means clustering in two congeneric classes.

Is soft independent modeling of class analogies a reasonable choice for supervised pattern recognition?

A thorough survey of classification data sets and a rigorous comparison of classification methods show the unambiguous superiority of other techniques over soft independent modeling of class analogies (SIMCA – one class modeling) for classification.

A comparative study of chromatographic behavior and lipophilicity of selected natural styryl lactones, their derivatives and analogues

This study is based on the analyses of the retention behavior of selected natural styryl lactones and their synthetic analogues in reversed-phase high-performance liquid chromatography. Chromatographic separations were achieved applying ZORBAX SB-C18 column and two different mobile phases: methanol-water and acetonitrile-water. Chromatographic lipophilicity of the analyzed compounds was defined by logk₀ constant and correlated with in silico molecular descriptors. According to the statistical validation parameters, obtained results indicate that the presented linear and multiple quantitative structure-retention relationship models can successfully predict the chromatographic lipophilicity of structurally similar compounds. Hierarchical cluster analyses (HCA) was applied in order to group similar compounds according to their chromatographic and in silico lipophilicity. It can be concluded that chromatographic systems with methanol-water were better for modelling of logk₀. Modelling was performed in order to characterize compounds regarding their lipophilicity profiles as future drug candidates.

Consensus-based comparison of chromatographic and computationally estimated lipophilicity of benzothiepino[3,2-c]pyridine derivatives as potential antifungal drugs

Lipophilicity is one of the essential properties influencing drug absorption, excretion and metabolism. It is used for screening viable drug candidates. Chromatographic behavior of thiepino[3,2-c:6,7-c']dipyridine and 16 benzothiepino[3,2-c]pyridine derivatives as potential antifungal drugs was studied using thin-layer chromatography under typical reversed-phase conditions and two microemulsion chromatographic systems. Seventeen chromatographic and nine in silico lipophilicity measures were estimated. They were compared by classical multivariate approaches: principal component analysis, hierarchical cluster analysis, and ranked and grouped by the non-parametric method-Sum of ranking differences. Two computational and two chromatographic descriptors from the typical reversed-phase conditions using acetone/water mixtures emerged as the best candidates for lipophilicity estimation. The principal component scores related to typical reversed-phase conditions using dioxane/water were ranked as statistically insignificant (the worst). Microemulsion systems were positioned in between, performing worse than in silico estimates. Thiepine derivatives were ranked and grouped by sum of ranking differences, fusing multiple lipophilicity measures. In multicriteria maximization ranking, the compound substituted by phenyl group at position 8 was selected as the most lipophilic one. It is also the most active against Candida albicans. The ranking confirmed that introduction of phenyl core is essential for increasing the lipophilicity of the studied compounds.

Assessment of Lipophilicity Indices Derived from Retention Behavior of Antioxidant Compounds in RP-HPLC

Reverse phase high pressure liquid chromatography was employed in order to evaluate the lipophilicity of antioxidant compounds from different classes, such as phenolic acids, flavanones, flavanols, flavones, anthocyanins, stilbenes, xantonoids, and proanthocyanidins. The retention time of each compound was measured using five different HPLC columns: RP18 (LiChroCART, Purosphere RP-18e), C8 (Zorbax, Eclipse XDBC8), C16-Amide (Discovery RP-Amide C16), CN100 (Saulentechnik, Lichrosphere), and pentafluorophenyl (Phenomenex, Kinetex PFP), and the mobile phase consisted of methanol and water (0.1% formic acid) in different proportions. The measurements were conducted at two different column temperatures, room temperature (22 °C) and, in order to mimic the environment from the human body, 37 °C. Furthermore, principal component analysis (PCA) was used to obtain new lipophilicity indices and holistic lipophilicity charts. Additionally, highly representative depictions of the chromatographic behavior of the investigated compounds and stationary phases at different temperatures were obtained using two new chemometric approaches, namely two-way joining cluster analysis and sum of ranking differences.