Theoretical Descriptors Research Articles

A theoretical descriptor for screening efficient NO reduction electrocatalysts from transition-metal atoms on N-doped BP monolayer

An electrochemical nitric oxide (NO) reduction reaction (NORR) is proposed as an attractive method for simultaneous realization of NO removal and ammonia (NH3) synthesis. Here, the potentials of 29 transition-metal atoms anchored on the nitrogen-doped BP monolayer (MN3/BP) as efficient NORR catalysts are systematically examined using first-principles calculations. Combining the adsorption Gibbs free energies of the N and OH species, a simple descriptor is constructed and a volcano plot of the NORR limiting potentials on the single atom catalysts (SACs) is established. Consequently, the MoN3/BP and IrN3/BP SACs are picked out as promising NORR electrocatalysts for NH3 synthesis with the limiting potentials of −0.10 V and −0.06 V, respectively. Their corresponding rate constants are significantly larger than or close to that of the excellent Pt(111) surface. The electronic analysis shows that the Mo-4d or Ir-5d orbitals can be well hybridized with the NO-2p orbitals, sufficiently activating the adsorbed NO species. Particularly, the MoN3/BP and IrN3/BP SACs possess high thermal stabilities and can be easily synthesized by using MoCl3 and IrCl3 as precursors, respectively. This work not only offers a simple descriptor to efficiently design NORR electrocatalysts but also provides a comprehensive atomic understanding on the mechanism of NO-to-NH3 conversion.

(3-(4-klorofenil)-4,5-dihidroizoksazol-4,5-diil)dimetanol Bileşiği: Antibakteriyel Aktivite, Antifungal Aktivite ve Hesaplanmış Yapısal Parametreler

We intended to quantify the antibacterial and antifungal activity results of 4,5-dihydroisoxazole dimethanol compound (1a) against several bacteria and fungi and also calculate some structural parameters (theoretical descriptors) of compound (1a) with this work. Microdilution broth procedures were studied using microdilution wells for the minimal inhibitory concentrations (MICs) test. Compound (1a) exhibited fair activities against all the bacteria and fungi. Compound (1a) has been a good result (MIC = 50 µg/ml) against particularly P. aeruginosa. The structure of compound (1a) was drawn, and geometrical optimization was done using the Ab initio (RHF/3-21G) level.

Evaluating micellar liquid chromatographic methods on octadecyl particle-based and monolithic columns to predict the skin permeation of drug and cosmetic molecules

A micellar liquid chromatographic method was developed to assist in the modeling of the skin permeability of pharmaceutical and cosmetic compounds. The composition of the mobile phase was determined by means of a two-factor central composite design, after which it was tested on both a particle-based and monolithic column. The latter provided the opportunity to increase the flow rate from 1 to 8 mL/min without reaching too high backpressures. The micellar conditions allowed analyzing a large test set of compounds with diverse characteristics with just one mobile-phase composition. The obtained experimental chromatographic descriptors besides two sets of theoretical molecular descriptors were used to model the skin permeability coefficient log Kp, applying multiple linear regression and partial least squares regression approaches. The micellar method on the monolithic column provided useful models with similar or even slightly better performance parameters than the method on the particle-based column. Furthermore, a much faster analysis can be achieved when applying a flow rate of 8 mL/min, making the micellar monolithic method ideal to estimate skin permeability.

Exploring Strategies for Developing Entrepreneurial Behavior of Students in Romanian Universities

Abstract Throughout this research paper we want to emphasize the correlations of theory and practice regarding the topic of the entrepreneurial university. The research combines a two-level approach by correlating both a qualitative-bibliometric analysis and a quantitative exploratory analysis. To identify the theoretical descriptors of the “entrepreneurial university” concept we performed a cluster analysis by using VOSviewer software selecting the published articles in the Scopus database since its beginning. The relevance of this analysis consists in the opportunity of extracting the most relevant information on the analyzed topic according to several criteria such as keywords, co-occurrence, co-citation, or publication topics by year. From this perspective the pieces of evidence are meaningful and we shall present them within the sections of the paper. On the other hand, for obtaining a practice grounded perspective, we applied 350 questionnaires to a target group involved in a university grant for young entrepreneurs. The purpose of the survey was to determine a series of characteristics and patterns of behavior of respondents related to their entrepreneurial orientation. The questionnaire contained a large number of variables for each individual to identify his/her distinct options and thus to draw the big picture. The variables were grouped into 6 dimensions which we identified as a priority for the entrepreneurial orientation framework: personal traits, motivation, attitude, framework conditions, and skills and knowledge. We statistically processed the survey data to identify the most meaningful correlations and interdependencies between the variables. The results of both research protocols are correlated and argued within the final section of the paper followed by conclusions and further research tracks.

Free energy difference to create the M-OH* intermediate of the oxygen evolution reaction by time-resolved optical spectroscopy.

Theoretical descriptors differentiate the catalytic activity of materials for the oxygen evolution reaction by the strength of oxygen binding in the reactive intermediate created upon electron transfer. Recently, time-resolved spectroscopy of a photo-electrochemically driven oxygen evolution reaction followed the vibrational and optical spectra of this intermediate, denoted M-OH*. However, these inherently kinetic experiments have not been connected to the relevant thermodynamic quantities. Here we discover that picosecond optical spectra of the Ti-OH* population on lightly doped SrTiO3 are ordered by the surface hydroxylation. A Langmuir isotherm as a function of pH extracts an effective equilibrium constant relatable to the free energy difference of the first oxygen evolution reaction step. Thus, time-resolved spectroscopy of the catalytic surface reveals both kinetic and energetic information of elementary reaction steps, which provides a critical new connection between theory and experiment by which to tailor the pathway of water oxidation and other surface reactions.

Water Activity Prediction in Sugar and Polyol Systems Using Theoretical Molecular Descriptors.

Water activity is a key factor in the development of pharmaceutical, cosmetic, and food products. In aqueous solutions of nonelectrolytes, the Norrish model provides a simple and effective way to evaluate this quantity. However, it contains a parameter, known as the Norrish constant, that must be estimated from experimental data. In this study, a new strategy is proposed for the prediction of water activity in the absence of experimental information, based on the use of theoretical molecular descriptors for characterizing the effects of a solute. This approach was applied to the evaluation of water activity in the presence of sugars (glucose, fructose, xylose, sucrose) and polyols (sorbitol, xylitol, glycerol, erythritol). The use of two descriptors related to the constitutional and connectivity properties of the solutes was first investigated. Subsequently, a new theoretical descriptor, named the global information index (G), was developed. By using this index, the water activity curves in the binary systems were reconstructed. The positive results obtained support the proposed strategy, as well as the possibility of including, in a single information index, the main molecular features of a solute that determine its effects on water activity.

Covalently Bonded Fullerene Nano-Aggregates (C60)n: Digitalizing Their Energy–Topology–Symmetry

Fullerene dimers and oligomers are attractive molecular objects with an intermediate position between the molecules and nanostructures. Due to the size, computationally assessing their structures and molecular properties is challenging, as it currently requires high-cost quantum chemical techniques. In this work, we have jointly studied energies, topological (Wiener indices and roundness), and information theoretic (information entropy) descriptors, and have obtained regularities in triad ‘energy–topology–symmetry’. We have found that the topological indices are convenient to indicating the most and least reactive atoms of the fullerene dimer structures, whereas information entropy is more suitable to evaluate odd–even effects on the symmetry of (C60)n. Quantum chemically assessed stabilities of selected C120 structures, as well as linear and zigzag (C60)n, are discussed.

QSAR Analysis of Quinazolinyl-arylurea Derivatives as Potential Anti- Cancer Agents: GA-MLR Chemometric Approach

Background: Cancer is the most common malignancy in men and women globally. The tyrosine kinases and serine/threonine kinases are essential to cell mediators for extra & intracellular signal transduction processes and play a key role in cell proliferation, differentiation, migration, metabolism, and programmed cell deaths. In this context, kinases are considered as a potential drug target for cancer therapy. Methods: In the present study, a two-dimensional (2D) quantitative structure-activity relationship (2D-QSAR) was performed to analyze anticancer activities of 28 quinazolinyl-arylurea (QZA) derivatives based on the liver (BEL-7402), stomach (MGC-803), and colon (HCC-827) cancer cell lines using multiple linear regression (MLR) analysis. It was accomplished using 2D-QSAR analysis on the available IC50 data of 28 molecules based on theoretical molecular descriptors to develop predictive models that correlate structural features of QZA derivatives to their anticancer activities. A suitable set of molecular descriptors, such as constitutional, topological, geometrical, electrostatic, and quantum-chemical descriptors were calculated to represent the structural features of compounds. The genetic algorithm (GA) method was used to identify the important molecular descriptors to build the QSAR models and used to predict the anti-cancer activities. Results and Discussion: The obtained 2D-QSAR models were vigorously validated using various statistical metrics using leave-one-out (LOO) and external test set prediction approaches. The best predictive models by MLR gave highly significant square of correlation coefficient (R2 train) values of 0.799, 0.815, and 0.779 for the training set, and the correlation coefficients (R2 test) were obtained 0.885, 0.929, and 0.774 for the test set for the liver, stomach, and colon cancer cell lines. The models also demonstrated good predictive power confirmed by the high value of cross-validated correlation coefficient Q2 value of 0.663, 0.717, and 0.671 for three different cancer cell lines. Importantly, the model's quality was judged as well based on mean absolute error (MAE) criteria, and the results were consistent with proposed limits by Golbraikh and Tropsha. Conclusion: The QSAR results of the study indicated that the proposed models were robust and free from chance correlation. This study indicated that maxHBint7, SpMax8_Bhm, and ETA_Beta_ns_d have positively contributed descriptors for anti-cancer activity in the liver, stomach, and colon cancer cell lines and a detailed mechanistic interpretation of each model revealed important structural features that were responsible for favorable or unfavorable for anticancer activity. The predictive ability of the proposed models was good and may be useful for developing more potent quinazolinyl-arylurea compounds as anti-cancer agents.

Persistent organic pollutants (POPs) - QSPR classification models by means of Machine learning strategies

Persistent Organic pollutants (POPs) are toxic chemicals with a shallow degradation rate and global negative impact. Their physicochemical is combined with the complex effects of long-term POPs accumulation in the environment and transport function through the food chain. That is why POPs have been linked to adverse effects on human health and animals. They circulate globally via different environmental pathways, and could be detected in regions far from their source of origin.The primary goal of the present study is to carry out classification of various representatives of POPs using different theoretical descriptors (molecular, structural) to develop quantitative structure−properties relationship (QSPR) models for predicting important properties POPs. Multivariate statistical methods such as hierarchical cluster analysis, principal components analysis and self-organizing maps were applied to reach excellent partitioning of 149 representatives of POPs into 4 classes using ten most appropriate descriptors (out of 63) defined by variable reduction procedure.The predictive capabilities of the defined classes could be applied as a pattern recognition for new and unidentified POPs, based only on structural properties that similar molecules may have.The additional self-organizing maps technique made it possible to visualize the feature-space and investigate possible patterns and similarities between POPs molecules. It contributes to confirmation of the proper classification into four classes. Based on SOM results, the effect of each variable and pattern formation has been presented.

Functionally substituted arylhydrazones as building blocks in heterocyclic synthesis: Facile synthesis of pyrazoles, triazoles, triazines and quantum chemical studies

The arylhydrazones were treated with ethyl iodide, ethyl chloroacetate to afford the alkyl derivatives, which cyclized to the pyrazole derivatives, also, arylhydrazones were reacted with ethyl chloroformate to furnish the triazindione derivative. Reactions of arylhydrazones with hydroxylamine hydrochloride afforded the oxime derivative. Treatment of arylhydrazones with binucleophile reagents such as hydrazine hydrate and phenyl hydrazine gives the pyrazole derivatives. Treated of arylidine derivatives with malononitrile and hydrazine hydrate afforded the 4H-pyrane derivatives and pyrazole derivatives. The theoretical calculations optimization and frequency of synthesized compounds by Gaussian 09 with DFT by B3LYP method. The base set 6–31 G (d,p) to obtain descriptors explains the reactivity or stability of the synthesized compounds. It predicts the relation between theoretical descriptors computed which agree with experimental data.

Mapping experimental and theoretical reactivity descriptors of fe macrocyclic complexes deposited on graphite or on multi walled carbon nanotubes for the oxidation of thiols: Thioglycolic acid oxidation

We have studied the electro-oxidation of thioglycolic acid (TGA) catalyzed by iron phthalocyanines and iron porphyrins (FeN4 complexes) deposited on ordinary pyrolytic graphite and on multiwalled carbon nanotubes. The purpose of this work is to establish both experimental and theoretical reactivity descriptors of MN4 macrocyclic complexes for electrooxidation of thioglycolic acid (TGA) as an extension of previous studies involving other reactions using these types of catalysts. Essentially, the reactivity descriptors are all related to the ability of the metal center in the MN4 moiety to coordinate an extra planar ligand that corresponds to the reacting molecule. This coordinating ability, represented by the M-TGA binding energy can be modulated by tuning the electron-donation ability of the ligand and it is linearly correlated with the Fe(III)/(II) redox potential of the complex. Experimental plots of activity as (log j)E at constant potential versus the Fe(III)/(II) redox potential of the MN4 catalysts give volcano correlations. A semi-theoretical plot of catalytic activities (log j)E vs DFT calculated Fe-TGA binding energies (EbTGA) is consistent with the experimental volcano-type correlations describing both strong and weak binding linear correlations of those volcanos. On the other hand, the Hirshfeld population analysis shows a positive charge on the Fe center of the FeN4 complexes, indicating that electron transfer occurs from the TGA to the Fe center in the FeN4 complexes that act as electron acceptors. The donor (TGA)-acceptor (Fe) intermolecular hardness ΔηDA was also used as reactivity descriptor and the reactivity of the Fe centers as (log j)E increase linearly as ΔηDA increases. If activity is considered per active site, the trends is exactly the opposite, i.e. a plot of (logTOF)E increases linearly as ΔηDA decreases as expected form the Maximum Hardness-Principle. A plot of (logTOF)E versus E°’Fe(III)/(II) gives a linear correlation indicating that the activity per active site increases as the redox potential decreases.

On Ve-Degree and Ev-Degree Topological Properties of Hyaluronic Acid‐Anticancer Drug Conjugates with QSPR

The design of the quantitative structure-property/activity relationships for drug-related compounds using theoretical methods relies on appropriate molecular structure representations. The molecular structure of a compound comprises all the information required to determine its chemical, biological, and physical properties. These properties can be assessed by employing a graph theoretical descriptor tool widely known as topological indices. Generalization of descriptors may reduce not only the number of molecular graph-based descriptors but also improve existing results and provide a better correlation to several molecular properties. Recently introduced ve-degree and ev-degree topological indices have been successfully employed for development of models for the prediction of various biological activities/properties. In this article, we propose the general ve-inverse sum indeg indexISIα,βveGand general ve-Zagreb indexMαveGof graphGand computeISIα,βveG,MαveG, andMαevG(general ev-degree index) of hyaluronic acid-curcumin/paclitaxel conjugates, renowned for its potential anti-inflammatory, antioxidant, and anticancer properties, by using molecular structure analysis and edge partitioning technique. Several ve-degree- and ev-degree-based topological indices are obtained as a special case ofISIα,βveG,MαveG, andMαevG. Furthermore, QSPR analysis ofISIα,βveG,MαveG, andMαevGfor particular values ofαandβis performed, which reveals their predicting power. These results allow researchers to better understand the physicochemical properties and pharmacological characteristics of these conjugates.

Designing of Highly Efficient Oxygen Evolution Reaction Electrocatalysts Utilizing A Correlation Factor: Theory and Experiment.

The theoretical prediction of the catalytic activity is very beneficial for the design of highly efficient catalysts. At present, most theoretical descriptors focus on estimating the catalytic activity and understanding the enhancement mechanism of catalysts, while it is also quite important to find a factor to correlate the descriptors with preparation methods. In this work, a correlation factor, the d electron density of transition metal ions, was developed to correlate the d band center values of transition metal ions with the preparation methods of amorphization and Al introduction. According to the results of theoretical simulations, the correlation factor not only exhibited favorable linear relationships with the theoretical overpotentials of (CoFeAlx)3O4 and (CoFeAlx)3O4 + (CoFeAlx)OOH systems but also correlated with two preparation methods by altering the volume of systems. Based on theoretical guidance, the electrocatalytic activities of the prepared (CoFeAlx)3O4 specimens were gradually improved by the preparation methods of amorphization and Al introduction, and the Am-CoFeAl-2-10h specimen exhibited a low kinetic barrier of 268 mV, fast charge transfer rate, and stable electrocatalytic activity. This strategy could be applied to design highly efficient catalysts by adjusting the correlation factor of the active site with suitable preparation methods.

Topological electron density properties at critical points along aromatic rings as reactivity and regioselectivity descriptors in electrophilic substitutions

AbstractIn order to evaluate the substituent effect on the reactivity and regioselectivity of aromatic rings with respect to electrophilic substitution reactions, we considered several benzene derivatives containing activating (−NH2, −NHCH3, −N(CH3)2, −OH, −OCH3, and −CHCH2) and deactivating (−F, −Cl, −Br, −COH, −COCH3, −COOCH3, −COOH, −COCl, −CN, and −NO2) groups. In this sense, we propose alternative theoretical descriptors of reactivity and regioselectivity, which are based only on electron density properties of initial substrates. The descriptors investigated here were obtained from the quantum theory of atoms in molecules (electron density curvatures at the ring critical point [RCP] and at the critical points of carbon nuclei along with kinetic and potential energy densities at the RCP) and from the interacting quantum atom formalism (intra‐atomic energy values of carbon atoms). The topological parameters at the RCPs and at carbon nuclei within the aromatic moiety were particularly successful in this task, being able to provide quantitative predictions of reactivity as compared with traditional resonance parameters used to that same end.

Theoretical evidence for the resonance-inhibited hydrogen bonding (RIHB) in enol-imine tautomers

• Resonance inhibited hydrogen bonds (RIHBs) can exist in some of enol-imine tautomers. • Generally in an RIHB system, π-resonance attempts to decrease the hydrogen bond strength, while σ-planarity can more or less compensate for this impairment. • Π-electron donating groups (EDGs) can strengthen an RIHB by hindering the resonance flow in a part of the molecule. • The ability of an EDG to tune an RIHB is enhanced with increasing the magnitude of its Hammet resonance constant. The theory of resonance-inhibited hydrogen bonding (RIHB) is tested on some illustrative examples with enol-imine tautomerism. In contrast to the resonance-assisted hydrogen bonding (RAHB), theoretical descriptors show that here, π-resonance weakens the intramolecular hydrogen bond (IMHB) strength in the given structures. The results show that it is possible to tune the strength of RIHB by substitution in a suitable position in the molecule. Once a substituent hiders the original resonance in the RIHB system, it leads to reinforcement of IMHB and vice versa. These effects mostly are enhanced with increasing the magnitude of Hammet resonance constants. Interestingly, the IMHB becomes 0.08 Å shorter, and thus quite stronger by using a powerful π-electron donating group like –O − on the basis of the RIHB theory. In general, RIHB and RAHB complement each other well, and provide a very good insight into the interplay between the resonance and hydrogen bonds.

Quantitatively modeling of tetracycline photodegradation in low molecular weight organic acids under simulated sunlight irradiation

As the ubiquitous active components in aquatic environments, low molecular weight organic acids (LMWOAs) have a large influence on the environmental behaviors of contaminants. This research was focused on the effect of different LMWOAs including 11 aliphatic acids and 7 aromatic acids on the photodegradation kinetics of tetracycline (TC), and the development of quantitative structure-activity relationship (QSAR) model. Results showed that TC photodegradation in the presence of LMWOAs fitted pseudo-first-order photolysis kinetics, and the observed photolysis rate constant (kobs) varied from 0.077 to 0.331 h−1. The QSAR model was developed by partial-least-squares (PLS) with using a sequential approach with 25 theoretical molecular descriptors. Four descriptors including ELUMO-EHOMO, ELUMO, CCR and Qmax were found to mechanistically and statistically affect kobs.The high cross validated regression coefficient (Qcum2, 0.898) and high correlation coefficient (R2, 0.908) indicated significantly goodness-of-fit and high robustness of the model. The predicted and observed values with high agreement in the defined applicability domain featured accuracy and feasibility of model. This work provided a robust predictive method for estimating the TC photodegradation in the presence of different structures of LMWOAs.

Chemometric analysis of bio-inspired micellar electrokinetic chromatographic systems – modelling of retention mechanism and prediction of biological properties using bile salts surfactants

The development of screening methods for the estimation of physicochemical, biological and pharmacokinetic properties of new pharmaceutical agents at early stage of the drug discovery and development process is highly desirable. Due to inadequate properties many of drug candidates are rejected during clinical trials. Among available screening methods, both chromatographic and electrophoretic approaches have a well-established position. This work is continuation of our research focused on developing new micellar electro-kinetic chromatographic methods for rapid assessment of complex biological properties. In this work an extended set of 45 compounds was used in order to realize two main goals. The first one was the characterization of molecular mechanism of retention in sodium cholate and sodium deoxycholate MEKC systems using quantitative structure-retention relationships (QSRR) approach. The second goal was evaluation of the possibilities to use MEKC with sodium cholate and sodium deoxycholate pseudo-stationary phases in order to predict human intestinal absorption (HIA). The proposed quantitative retention-activity relationships (QRAR) model was additionally supported by theoretical descriptors and support vector machine (SVM) regression. Additionally, the prediction ability of obtained QRAR models were compared with PREADME software. The obtained results suggest that SVM model has better predictive abilities and is better suited to separate noise which accompanies the experimental and computational descriptors.

Improved Prediction of Carbonless NMR Spectra by the Machine Learning of Theoretical and Fragment Descriptors for Environmental Mixture Analysis.

As the first multidimensional NMR approach, 2D J-resolved (2DJ) spectroscopy is distinguished by signal resolution and detection sensitivity with remarkable advantages for the exhaustive evaluation of complex mixtures and environmental samples due to its carbonless feature without the requirement of 13C connectivity. Generally, the 2DJ signal assignment of metabolic mixtures is problematic in spite of references to experimental NMR databases, owing to the existence of metabolic "dark matter." In this study, a new method to predict 2DJ spectra was developed with a combination of quantum mechanical (QM) computation and machine learning (ML). The predictive accuracy of J-coupling constants was evaluated using validated data. The root-mean-square deviation (RMSD) for QM computation was 3.52 Hz, while the RMSD for QM + ML was 1.21 Hz, indicating a substantial increase in predictive accuracy. The proposed model was applied to predict the 2DJ spectra of 60 standard substances and 55 components of seawater. Furthermore, two practical environmental samples were used to evaluate the robustness of the constructed predictive model. A J-coupling tree and J-split spectra produced from QM + ML of aliphatic moieties had good consistency with the experimental data, as compared with the theoretical data produced by QM computation. The predicted J-coupling tree for the J-coupling multiplet analysis of freely rotating bonds in the complex mixture, which is traditionally difficult, was interpretable. In addition, in silico identification of the J-split 1H NMR signals, which was independent of experimental databases, aided in the discovery of new components in a mixture.

Comparison of in-silico modelling and reversed-phase liquid chromatographic retention on an octadecyl silica column to predict skin permeability of pharmaceutical and cosmetic compounds

This study focuses on the in-silico modelling of the skin permeability using a test set of pharmaceutical and cosmetic compounds. Two sets of theoretical molecular descriptors, obtained from the E-Dragon and Vega ZZ software programs, were used in the models. Different linear regression methods, i.e. Multiple Linear Regression (MLR) and Partial Least Squares (PLS) regression, were applied for modelling and estimating the skin permeability. The best model was obtained using a stepwise MLR approach on the E-Dragon descriptor set. In a second step, the retention of the test set compounds was measured on a C18 column at two pH levels: pH 5.5 and pH 7. Different organic-modifier fractions were applied in the mobile phase to be able to extrapolate the retention factors to a log kw value, with kw the estimated retention factor in an aqueous mobile phase without organic modifier. Thereafter it was examined whether combining this chromatographic descriptor with the theoretical descriptors could improve the modelling of the skin permeability. The chromatographic descriptor often did not show an added value compared to the models containing only theoretical descriptors. Therefore, the in-silico models were preferred, and these models could be useful to predict the skin permeability of pharmaceutical and cosmetic compounds.

Interaction between Antifungal Isoxazolo[3,4-b]Pyridin 3(1H)-One Derivatives and Human Serum Proteins Analyzed with Biomimetic Chromatography and QSAR Approach

The development of effective, nontoxic antifungal agents is one of the most important challenges for medicinal chemistry. A series of isoxazolo [3,4-b]pyridine-3(1H)-one derivatives previously synthesized in our laboratory demonstrated promising antifungal properties. The main goal of this study was to investigate their retention behavior in a human serum proteins-high-performance liquid chromatography (HSA-HPLC) system and explore the molecular mechanism of HSA-isoxazolone interactions using a quantitative structure–retention relationship (QSRR) approach. In order to realize this goal, multiple linear regression (MLR) modeling has been performed. The proposed QSRR models presented correlation between experimentally determined lipophilicity and computational theoretical molecular descriptors derived from Dragon 7.0 (Talete, Milan, Italy) software on the affinity of isoxazolones to HSA. The calculated plasma protein binding (PreADMET software) as well as chromatographic lipophilicity (logkw) and phospholipophilicity (CHIIAM) parameters were statistically evaluated in relation to the determined experimental HAS affinities (logkHSA). The proposed model met the Tropsha et al. criteria R2 > 0.6 and Q2 > 0.5 These results indicate that the obtained model can be useful in the prediction of an affinity to HSA for isoxazolone derivatives and they can be considered as an attractive alternative to HSA-HPLC experiments.