QSAR Approach Research Articles

Investigation of the Anticancer Potential of 2-alkoxycarbonylallyl Esters Against Metastatic Murine Breast Cancer Line 4T1 Targeting the EGFR: A Combined Molecular Docking, QSAR, and Machine Learning Approach

Background:The search for novel and potent anticancer drugs is imperative. This present study aims to unravel the mechanisms of action of 2-alkoxyecarbonyl esters using robust model(s) that can accurately predict the bioactivity of novel compounds. Twenty-four potential anticancer 2- alkoxycarbonylallyl ester compounds obtained from the literature were employed in building a 3D-QSAR model.Objectives:The objective of this study is to determine the predictive ability of the GFA-based QSAR models and extreme machine learning models and compare them. The lead compounds and newly designed compounds were docked at the active site of a human epidermal growth factor receptor (EGFR) kinase domain to determine their binding modes and affinity.Methods:QikProp program and Spartan packages were employed for screening compounds for druglikeness and toxicity. QSAR models were equally used to predict the bioactivities of these molecules using the Material Studio package. Molecular docking of the molecules at the active site of an EGFR receptor, 1M17, was done using Auto dock tools.Results:The model of choice, with r2pred (0.857), satisfied the recommended standard for a stable and reliable model. The low value of r2, Q2 for several trials and cRp2 (0.779 ≥ 0.5) and the high value of correlation coefficient r2 for the training set (0.918) and test set (0.849) provide credence to the predictability of the model. The superior inhibition of EGFR displayed by the lead compounds (20 and 21) with binding energies of 6.70 and 7.00 kcalmol-1, respectively, is likely due to the presence of double bonds and α-ester groups. ADMET screening showed that these compounds are highly druggable. The designed compounds (A and B) displayed better inhibition of EGFR.Conclusion:The QSAR model used here performed better than the Random Forest Regression model for predicting the bioactivity of these anticancer compounds, while the designed compounds (A and B) performed better with higher binding affinity than the lead compounds. Implementing the developed model would be helpful in the search for novel anticancer agents.

A Combined QSAR and Molecular Docking Approach for Identifying Pyrimidine Derivatives as Penicillin Binding Protein Inhibitors

Background:Antimicrobial resistance has been rising continuously in the past few years due to the overuse and exploitation of existing antimicrobials. This has motivated the search for a novel scaffold that has the capability of rapid antimicrobial action. The hybridized pyrimidines have attracted us due to their widespread biological activities, such as anti-bacterial and antifungal activities.Objective:The present study incorporates a series of pyrimidine-based antimicrobial agents for the 2D quantitative structure-activity relationship analysis (2D QSAR) and docking analysis.Methods:The exploration of the chemical structures in combination with the biological activity in CPMLR led to the detection of six descriptors (Constitutional descriptors, Topological descriptors, Modified Burden Eigenvalues and 2D autocorrelations) for modeling the activity. The resulted QSAR model has been validated using combinatorial protocol in multiple linear regression (CP-MLR) and partial least squares (PLS) analysis.Methods:The exploration of the chemical structures in combination with the biological activity in CPMLR led to the detection of six descriptors (Constitutional descriptors, Topological descriptors, Modified Burden Eigenvalues and 2D autocorrelations) for modeling the activity. The resulted QSAR model has been validated using a combinatorial protocol in multiple linear regression (CP-MLR) and partial least squares (PLS) analysis.Results:The best QSAR model displays the r2 t value of 0.594, Q2 LOO value of 0.779, Q2 L5O value of 0.767. Further docking study was executed using Autodock Vina against Penicillin-binding protein (PBP2a).Conclusion:From the results, Compounds 4, 11and 24 were found to possess a good binding affinity towards PBP2a.

Searching glycolate oxidase inhibitors based on QSAR, molecular docking, and molecular dynamic simulation approaches

Primary hyperoxaluria type 1 (PHT1) treatment is mainly focused on inhibiting the enzyme glycolate oxidase, which plays a pivotal role in the production of glyoxylate, which undergoes oxidation to produce oxalate. When the renal secretion capacity exceeds, calcium oxalate forms stones that accumulate in the kidneys. In this respect, detailed QSAR analysis, molecular docking, and dynamics simulations of a series of inhibitors containing glycolic, glyoxylic, and salicylic acid groups have been performed employing different regression machine learning techniques. Three robust models with less than 9 descriptors—based on a tenfold cross (Q2CV) and external (Q2EXT) validation—were found i.e., MLR1 (Q2CV = 0.893, Q2EXT = 0.897), RF1 (Q2CV = 0.889, Q2EXT = 0.907), and IBK1 (Q2CV = 0.891, Q2EXT = 0.907). An ensemble model was built by averaging the predicted pIC50 of the three models, obtaining a Q2EXT = 0.933. Physicochemical properties such as charge, electronegativity, hardness, softness, van der Waals volume, and polarizability were considered as attributes to build the models. To get more insight into the potential biological activity of the compouds studied herein, docking and dynamic analysis were carried out, finding the hydrophobic and polar residues show important interactions with the ligands. A screening of the DrugBank database V.5.1.7 was performed, leading to the proposal of seven commercial drugs within the applicability domain of the models, that can be suggested as possible PHT1 treatment.

Ligand-based and structure-based studies to develop predictive models for SARS-CoV-2 main protease inhibitors through the 3d-qsar.com portal

The main protease (Mpro) of SARS-Cov-2 is the essential enzyme for maturation of functional proteins implicated in viral replication and transcription. The peculiarity of its specific cleavage site joint with its high degree of conservation among all coronaviruses promote it as an attractive target to develop broad-spectrum inhibitors, with high selectivity and tolerable safety profile. Herein is reported a combination of three-dimensional quantitative structure–activity relationships (3-D QSAR) and comparative molecular binding energy (COMBINE) analysis to build robust and predictive ligand-based and structure-based statistical models, respectively. Models were trained on experimental binding poses of co-crystallized Mpro-inhibitors and validated on available literature data. By means of deep optimization both models’ goodness and robustness reached final statistical values of r2/q2 values of 0.97/0.79 and 0.93/0.79 for the 3-D QSAR and COMBINE approaches respectively, and an overall predictiveness values of 0.68 and 0.57 for the SDEPPRED and AAEP metrics after application to a test set of 60 compounds covered by the training set applicability domain. Despite the different nature (ligand-based and structure-based) of the employed methods, their outcome fully converged. Furthermore, joint ligand- and structure-based structure–activity relationships were found in good agreement with nirmatrelvir chemical features properties, a novel oral Mpro-inhibitor that has recently received U.S. FDA emergency use authorization (EUA) for the oral treatment of mild-to-moderate COVID-19 infected patients. The obtained results will guide future rational design and/or virtual screening campaigns with the aim of discovering new potential anti-coronavirus lead candidates, minimizing both time and financial resources. Moreover, as most of calculation were performed through the well-established web portal 3d-qsar.com the results confirm the portal as a useful tool for drug design.Graphical abstract

Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning.

The toxicity, absorption, distribution, metabolism, and excretion properties of some targets are difficult to predict by quantitative structure–activity relationship analysis. Therefore, there is a need for a new prediction method that performs well for these targets. The aim of this study was to develop a new regression model of rat clearance (CL). We constructed a regression model using 1545 in-house compounds for which we had rat CL data. Molecular descriptors were calculated using molecular operating environment, alvaDesc, and ADMET Predictor software. The classification model of DeepSnap and Deep Learning (DeepSnap-DL) with images of the three-dimensional chemical structures of compounds as features was constructed, and the prediction probabilities for each compound were calculated. For molecular descriptor-based methods that use molecular descriptors and conventional machine learning algorithms selected by DataRobot, the correlation coefficient (R2) and root mean square error (RMSE) were 0.625–0.669 and 0.295–0.318, respectively. We combined molecular descriptors and prediction probability of DeepSnap-DL as features and developed a novel regression method we called the combination model. In the combination model with these two types of features and conventional algorithms selected by DataRobot, R2 and RMSE were 0.710–0.769 and 0.247–0.278, respectively. This finding shows that the combination model performed better than molecular descriptor-based methods. Our combination model will contribute to the design of more rational compounds for drug discovery. This method may be applicable not only to rat CL but also to other pharmacokinetic and pharmacological activity and toxicity parameters; therefore, applying it to other parameters may help to accelerate drug discovery.

QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach

Modern QSAR approaches have wide practical applications in drug discovery for designing potentially bioactive molecules. If such models are based on the use of 2D descriptors, important information contained in the spatial structures of molecules is lost. The major problem in constructing models using 3D descriptors is the choice of a putative bioactive conformation, which affects the predictive performance. The multi-instance (MI) learning approach considering multiple conformations in model training could be a reasonable solution to the above problem. In this study, we implemented several multi-instance algorithms, both conventional and based on deep learning, and investigated their performance. We compared the performance of MI-QSAR models with those based on the classical single-instance QSAR (SI-QSAR) approach in which each molecule is encoded by either 2D descriptors computed for the corresponding molecular graph or 3D descriptors issued for a single lowest energy conformation. The calculations were carried out on 175 data sets extracted from the ChEMBL23 database. It is demonstrated that (i) MI-QSAR outperforms SI-QSAR in numerous cases and (ii) MI algorithms can automatically identify plausible bioactive conformations.

Exploration of the structural requirements of Aurora Kinase B inhibitors by a combined QSAR, modelling and molecular simulation approach

Aurora kinase B plays an important role in the cell cycle to orchestrate the mitotic process. The amplification and overexpression of this kinase have been implicated in several human malignancies. Therefore, Aurora kinase B is a potential drug target for anticancer therapies. Here, we combine atom-based 3D-QSAR analysis and pharmacophore model generation to identify the principal structural features of acylureidoindolin derivatives that could potentially be responsible for the inhibition of Aurora kinase B. The selected CoMFA and CoMSIA model showed significant results with cross-validation values (q2) of 0.68, 0.641 and linear regression values (r2) of 0.971, 0.933 respectively. These values support the statistical reliability of our model. A pharmacophore model was also generated, incorporating features of reported crystal complex structures of Aurora kinase B. The pharmacophore model was used to screen commercial databases to retrieve potential lead candidates. The resulting hits were analyzed at each stage for diversity based on the pharmacophore model, followed by molecular docking and filtering based on their interaction with active site residues and 3D-QSAR predictions. Subsequently, MD simulations and binding free energy calculations were performed to test the predictions and to characterize interactions at the molecular level. The results suggested that the identified compounds retained the interactions with binding residues. Binding energy decomposition identified residues Glu155, Trp156 and Ala157 of site B and Leu83 and Leu207 of site C as major contributors to binding affinity, complementary to 3D-QSAR results. To best of our knowledge, this is the first comparison of WaterSwap field and 3D-QSAR maps. Overall, this integrated strategy provides a basis for the development of new and potential AK-B inhibitors and is applicable to other protein targets.

Multiple Conformer Descriptors for QSAR Modeling.

The most widely used QSAR approaches are mainly based on 2D molecular representation which ignores stereoconfiguration and conformational flexibility of compounds. 3D QSAR uses a single conformer of each compound which is difficult to choose reasonably. 4D QSAR uses multiple conformers to overcome the issues of 2D and 3D methods. However, many of existing 4D QSAR models suffer from the necessity to pre-align conformers, while alignment-independent approaches often ignore stereoconfiguration of compounds. In this study we propose a QSAR modeling approach based on transforming chirality-aware 3D pharmacophore descriptors of individual conformers into a set of latent variables representing the whole conformer set of a molecule. This is achieved by clustering together all conformers of all training set compounds. The final representation of a compound is a bit string encoding cluster membership of its conformers. In our study we used Random Forest, but this representation can be used in combination with any machine learning method. We compared this approach with conventional 2D and 3D approaches using multiple data sets and investigated the sensitivity of the approach proposed to tuning parameters: number of conformers and clusters.

PCAC: a new method for predicting compounds with activity cliff property in QSAR approach

PCAC: a new method for predicting compounds with activity cliff property in QSAR approach

Multi-QSAR approaches for investigating the relationship between chemical structure descriptors of Thiadiazole derivatives and their corrosion inhibition performance

In this study, different QSAR approaches based on the multiple linear regression (MLR), the kernel-based Partial Least-Square (kPLS) and Pharma-RQSAR models have been applied to investigate the relationship between chemical structure descriptors of thiadiazole derivatives and their anticorrosion activity. The screened classical descriptors were firstly used to establish the linear regression model. In addition, six binary fingerprints were successfully applied using the kernel-based Partial Least-Square (kPLS), with priority given to the atom-pair fingerprint model. The Pharma-RQSAR model was also used to analyze the role of the various substitutions in the common substructure. The atom-pair descriptors and Pharma-RQSAR models have the advantage of visualizing the contribution maps, suggesting the general substitution requirements of the thiadiazole derivatives as potential corrosion inhibitors.

Monte Carlo Optimization-Based QSAR Study of Some Indole-Based Mcl-1 Inhibitors

Myeloid cell leukemia (Mcl-1) protein, belonging to a large BCL2 family of proteins, is a significant target towards cancer chemotherapy. The over-expression of this protein is also responsible for the development and progression of severe multi-drug resistance in cancer patients. This present study has focused the attention towards unmasking of important structural fingerprints for promoting or hindering Mcl-1 inhibitory activity of some indole-based derivatives by using Monte Carlo optimization-based QSAR approach. Twenty-one robust classification models were generated. The best model (by using SMILES and HSG-based descriptors) was selected, and several important good and bad structural fingerprints for Mcl-1 inhibition were identified. Some of the fingerprints were matching with the earlier study (New J. Chem., 2020, 44, 17494-17506) and some new fingerprints were also generated. The modelling study will help the researchers in the lead optimization of some indole-based Mcl-1 inhibitors in the future.

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.

QSAR study of unsymmetrical aromatic disulfides as potent avian SARS-CoV main protease inhibitors using quantum chemical descriptors and statistical methods

In silico research was executed on forty unsymmetrical aromatic disulfide derivatives as inhibitors of the SARS Coronavirus (SARS-CoV-1). Density functional theory (DFT) calculation with B3LYP functional employing 6-311 ​+ ​G(d,p) basis set was used to calculate quantum chemical descriptors. Topological, physicochemical and thermodynamic parameters were calculated using ChemOffice software. The dataset was divided randomly into training and test sets consisting of 32 and 8 compounds, respectively. In attempt to explore the structural requirements for bioactives molecules with significant anti-SARS-CoV activity, we have built valid and robust statistics models using QSAR approach. Hundred linear pentavariate and quadrivariate models were established by changing training set compounds and further applied in test set to calculate predicted IC50 values of compounds. Both built models were individually validated internally as well as externally along with Y-Randomization according to the OECD principles for the validation of QSAR model and the model acceptance criteria of Golbraikh and Tropsha’s. Model 34 is chosen with higher values of R2, R2test and Q2cv (R2 ​= ​0.838, R2test ​= ​0.735, Q2cv ​= ​0.757).It is very important to notice that anti-SARS-CoV main protease of these compounds appear to be mainly governed by five descriptors, i.e. highest occupied molecular orbital energy (EHOMO), energy of molecular orbital below HOMO energy (EHOMO-1), Balaban index (BI), bond length between the two sulfur atoms (S1S2) and bond length between sulfur atom and benzene ring (S2Bnz). Here the possible action mechanism of these compounds was analyzed and discussed, in particular, important structural requirements for great SARS-CoV main protease inhibitor will be by substituting disulfides with smaller size electron withdrawing groups. Based on the best proposed QSAR model, some new compounds with higher SARS-CoV inhibitors activities have been designed. Further, in silico prediction studies on ADMET pharmacokinetics properties were conducted.

Prediction of aquatic toxicity of chemical mixtures by the QSAR approach using 2D structural descriptors

The rapid industrialization has led to the generation of various organic chemicals and multi-component mixtures which affect the environment adversely. Although organic chemicals are often exposed to the environment as a form of chemical mixtures rather than individual compounds, there is insufficient toxicity data available for the chemical mixtures due to the associated complexities. Most importantly, the nature of toxicity of mixtures is completely different from the individual chemicals, which makes the evaluation more difficult and challenging. In this paper, we have developed QSAR models for various individual and mixture data sets for the prediction of the aquatic toxicity. We have used Partial Least Squares (PLS) regression as a statistical tool to build the models. The various structural features of the individual chemicals and the mixture components have been modeled against the toxicity end point pEC50 (negative logarithm of median effective concentration in molar scale) of the aquatic organisms Photobacterium phosphoreum (marine bacterium) and Selenastrum capricornutum (freshwater algae). The mixture descriptors have been calculated by the weighted descriptor generation approach. The models were developed in accordance with OECD guidelines, and the quality of each model has been adjudged by strict validation parameters. The final models are robust, extremely predictive and interpretable mechanistically which can be used for the prediction of toxicity of untested chemical mixtures under the domain of applicability of the developed models.

Design New Compound of Meisoindigo Derivative as Anti Breast Cancer Based on QSAR Approach

Design new compounds of Meisoindigo using the QSAR approach with semiempirical methods have been successfully made with better biological activity as anti-breast cancer results. The research method used to design the new compound of meisoindigo derivatives uses the semiempirical methods. The research procedure divide into tree step, molecular modeling, QSAR equation analysis, and design of new compounds. The PM3 method was chosen as a better method because it has model results that are more representative of physicochemical aspects. The selection of the best model is made by multilinear regression statistical analysis. New compounds derived from the design are expected to bind to the Cyclin-Dependent Kinase 4 (CDK4) enzyme that helps in the cell cycle to prevent cell division. Based on the design, the compound of (E)-2-(2-oxo-1-(2-(trifluoromethyl) benzyl)indolin-3-ylidene)-N-(quinoline-7-yl) acetamide choose as a new compound with more better biological activity (log 1/IC50 = 6.992) than before (log 1/IC50 = 5.823)

Theoretical calculations of molecular descriptors for anticancer activities of 1, 2, 3-triazole-pyrimidine derivatives against gastric cancer cell line (MGC-803): DFT, QSAR and docking approaches

sThis work used quantum chemical method via DFT to calculate molecular descriptors for the development of QSAR model to predict bioactivity (IC50- 50% inhibition concentration) of the selected 1, 2, 3-triazole-pyrimidine derivatives against receptor (human gastric cancer cell line, MGC-803). The selected molecular parameters were obtained by B3LYP/6-31G∗∗. QSAR model linked the molecular parameters of the studied compounds to their cytotoxicity and reproduced their observed bioactivities against MGC-803. The calculated IC50 tailored the observed IC50 and greater than standard compound, 5-fluorouracil, suggesting that the developed QSAR model reproduced the observed bioactivity. Statistical analyses (including R2, CV. R2 and gave 0.950, 0.970 and 0.844 respectively) revealed a very good fitness. Molecular docking studies revealed the hydrogen bonding with the amino acid residues in the binding site, as well as ligand conformations which are essential feature for ligand-receptor interactions. Therefore, the methods used in this study are veritable tools that can be employed in pharmacological and medicinal chemistry researches in designing better drugs with improve potency.

Energetic contributions of amino acid residues and its cross-talk to delineate ligand-binding mechanism.

Receptor-based QSAR approaches can enumerate the energetic contributions of amino acid residues toward ligand binding only when experimental binding affinity is associated. The structural data of protein-ligand complexes are witnessing a tremendous growth in the Protein Data Bank deposited with a few entries on binding affinity. We present here a new approach to compute the Energetic CONTributions of Amino acid residues and its possible Cross-Talk (ECONTACT) to study ligand binding using per-residue energy decomposition, molecular dynamics simulations and rescoring method without the need for experimental binding affinity. This approach recognizes potential cross-talks among amino acid residues imparting a nonadditive effect to the binding affinity with evidence of correlative motions in the dynamics simulations. The protein-ligand interaction energies deduced from multiple structures are decomposed into per-residue energy terms, which are employed as variables to principal component analysis and generated cross-terms. Out of 16 cross-talks derived from eight datasets of protein-ligand systems, the ECONTACT approach is able to associate 10 potential cross-talks with site-directed mutagenesis, free energy, and dynamics simulations data strongly. We modeled these key determinants of ligand binding using joint probability density function (jPDF) to identify cross-talks in protein structures. The top two cross-talks identified by ECONTACT approach corroborated with the experimental findings. Furthermore, virtual screening exercise using ECONTACT models better discriminated known inhibitors from decoy molecules. This approach proposes the jPDF metric to estimate the probability of observing cross-talks in any protein-ligand complex. The source code and related resources to perform ECONTACT modeling is available freely at https://www.gujaratuniversity.ac.in/econtact/.

Anti-tubercular Modelling via QSAR Approach, In Silico Design and Virtual Docking Screening of Designed Hypothetical Inhibitors Against DNA gyrase Protein.

Mycobacterium tuberculosis has instigated a serious challenge toward the effective treatment of tuberculosis. The reoccurrence of the resistant strains of the disease to accessible drugs/medications has mandate for the development of more effective anti-tubercular agents with efficient activities. Time expended and costs in discovering and synthesizing new hypothetical drugs with improved biological activity have been a major challenge toward the treatment of multidrug resistance strain M. tuberculosis (TB). Meanwhile, to solve the stated problem, a new approach i.e. QSAR which establish connection between novel drugs with a better biological against M. tuberculosis is adopted. The anti-tubercular model established in this study to forecast the biological activities of some anti-tubercular compounds selected and to design new hypothetical drugs is subjective to the molecular descriptors; MATS7s, SM1_DzZ, SpMin4_Bhv, TDB3v and RDF70v. Ligand-receptor interactions between quinoline derivatives and the receptor (DNA gyrase) was carried out using molecular docking technique by employing the PyRx virtual screening software and discovery studio visualizer software. Furthermore, docking study indicates that compound 20 of the derivatives with promising biological activity has the utmost binding energy of -17.79 kcal/mol. Meanwhile, the interaction of the standard drug; isoniazid with the target enzyme was observed with the binding energy -14.6 kcal/mol which was significantly lesser than the binding energy of the ligand (compound 20). Therefore, compound 20 served as a template structure to design compounds with more efficient activities. Among the compounds designed; compounds 20p was observed with better anti-tubercular activities with more prominent binding affinities of - 24.3kcal/mol. The presumption of this research aid the medicinal chemists and pharmacist to design and synthesize a novel drug candidate against tuberculosis. Moreover, in-vitro and in-vivo test could be carried out to validate the computational results.

Identification and in silico prediction of metabolites of tebufenozide derivatives by major human cytochrome P450 isoforms

Cytochrome P450 (CYP) enzymes constitute a superfamily of heme-containing monooxygenases. CYPs are involved in the metabolism of many chemicals such as drugs and agrochemicals. Therefore, examining the metabolic reactions by each CYP isoform is important to elucidate their substrate recognition mechanisms. The clarification of these mechanisms may be useful not only for the development of new drugs and agrochemicals, but also for risk assessment of chemicals. In our previous study, we identified the metabolites of tebufenozide, an insect growth regulator, formed by two human CYP isoforms: CYP3A4 and CYP2C19. The accessibility of each site of tebufenozide to the reaction center of CYP enzymes and the susceptibility of each hydrogen atom for metabolism by CYP enzymes were evaluated by a docking simulation and hydrogen atom abstraction energy estimation at the density functional theory level, respectively. In this study, the same in silico prediction method was applied to the metabolites of tebufenozide derivatives by major human CYPs (CYP1A2, 2C9, 2C19, 2D6, and 3A4). In addition, the production rate of the metabolites by CYP3A4 was quantitively analyzed by frequency based on docking simulation and hydrogen atom abstraction energy using the classical QSAR approach. Then, the obtained QSAR model was applied to predict the sites of metabolism and the metabolite production order by each CYP isoform.

Exploring the structural aspects of ureido-amino acid-based APN inhibitors: a validated comparative multi-QSAR modelling study

ABSTRACT The zinc-dependent enzyme aminopeptidase N (APN) is a member of the M1 metalloenzyme family. The multi-functionality of APN as a peptidase, a receptor and a signalling molecule has provided it the access to influence a number of disease conditions namely viral diseases, angiogenesis, cellular metastasis and invasion including different cancer conditions. Hence, the development of potent APN inhibitors is a possible route for the treatment of diseases related to the activity of APN. In this study, different QSAR approaches have been adopted to identify the structural features of a group of hydroxamate-based ureido-amino acid derivative APN inhibitors. This study was able to identify different constitutional aspects of these APN inhibitors which are important for their inhibitory potency. Additionally, some of these observations were also aligned with the observations of previously performed QSAR studies conducted on different APN inhibitors. Therefore, the results of this study may help to design potent and effective APN inhibitors in the future.