Multivariate SAR/QSAR of 3-aryl-4-hydroxyquinolin-2(1H)-one derivatives as type I fatty acid synthase (FAS) inhibitors

Machine Learning-Based Virtual Screening of Antibacterial Agents against Methicillin-Susceptible and Resistant Staphylococcus aureus

The application of computer-aided drug discovery (CADD) approaches has enabled the discovery of new antimicrobial therapeutic agents in the past. The high prevalence of methicillin-resistantStaphylococcus aureus(MRSA) strains promoted this pathogen to a high-priority pathogen for drug development. In this sense, modern CADD techniques can be valuable tools for the search for new antimicrobial agents. We employed a combination of a series of machine learning (ML) techniques to select and evaluate potential compounds with antibacterial activity against methicillin-susceptible S. aureus (MSSA) and MRSA strains. In the present study, we describe the antibacterial activity of six compounds against MSSA and MRSA reference (American Type Culture Collection (ATCC)) strains as well as two clinical strains of MRSA. These compounds showed minimal inhibitory concentrations (MIC) in the range from 12.5 to 200 μM against the different bacterial strains evaluated. Our results constitute relevant proven ML-workflow models to distinctively screen for novel MRSA antibiotics.

Classification analysis of fatty acid synthase inhibitors using multialgorithms on topological descriptors and structural fingerprints

Fatty acid synthase (FASN) is one of the enzymes required for fatty acid biosynthesis and is expressed as low or absent in most normal cells/tissues. However, this enzyme is upregulated in various cancer cells; hence, it can act as an important target to design and develop novel FASN inhibitors for cancer therapy. In the present investigation, a series of structurally diverse compounds that possessed FASN inhibitory activities were subjected to classification analysis using different algorithms such as support vector machine, decision tree, Naïve Bayes and random forest. The physicochemical descriptors and MACCS fingerprints were calculated using PaDEL software, and the WEKA software was utilized for the classification model building. The statistical parameters/confusion matrix calculated from the analysis revealed that the selected models have significant predictive performances. The results showed that the topological properties of the molecules are the main determinant for the activity classification. The key descriptors comprised of hydrogen bonding groups, especially acceptor (nHBAcc, minHBint9, minHBint5 and nwHBa), charge on the topological surface of the molecules (JGI10 & GGI2), ionization potential (GATS5i and GATS1i) and branching and distance between the groups (ETA_Eta_B_RC) are significantly contributed in the classification models. Further, the presence of heteroatoms (MACCSFP82, MACCSFP93 and MACCSFP131), especially nitrogen atom(s) and hydrogen bond acceptor groups (N-N group, NC(=O)N, N-C(=O)), actively contributed to the inhibitory activities. The results concluded that the topological polar properties concentrated in a specific region have significant FASN inhibitory activity. Hence, these results shall be used to develop novel molecules with increased FASN inhibitory activity.

Screening and identification of 3-aryl-quinolin-2-one derivatives as antiviral agents against influenza A

Quinolin-2-one represents an important and valuable chemical motif that possesses a wide variety of biological activities; however, the anti-influenza activities of quinolin-2-one-containing compounds were rarely reported. Herein, we describe the screening and identification of 3-aryl-quinolin-2-one derivatives as a novel class of antiviral agents. The 3-aryl-quinolinone derivatives were synthesized via an efficient copper-catalyzed reaction cascade that we previously developed. Using this synthetic method, preliminary structure-activity relationships of this scaffold against the influenza A virus infection were systematically explored. The most potent compound 34 displayed IC₅₀ values of 2.14 and 4.88 μM against the replication of H3N2 (A/HK/8/68) and H1N1 (A/WSN/33) strains, respectively, without apparent cytotoxicity on MDCK cells. We further demonstrated that 27 and 34 potently inhibited the plaque formation of the IAV, rendering this scaffold attractive for pursuing novel anti-influenza agents.

Ring expansion and ring opening of 3-halooxindoles with N-alkoxycarbonyl-O-tosylhydroxylamines for divergent access to 4-aminoquinolin-2-ones and N-Cbz-N’-arylureas

The reaction of N-alkoxycarbonyl-O-tosylhydroxylamines with indol-2-ones in situ generated from 3-halooxindoles has been developed for divergently accessing 4-aminoquinolin-2-ones and N-Cbz-N’-arylureas in good to excellent yields.

Prediction of polydimethylsiloxane-water partition coefficients based on the pp-LFER and QSAR models

Obtaining accurate measurements of the partition coefficients between sorbent materials and water is of major importance for the analysis of the adsorption behavior and dissolved concentrations of organic compounds in the environment. In the passive-sampling approach, polydimethylsiloxane (PDMS) has a wide range of applications. Therefore, we established a poly-parameter linear-free energy relationship (pp-LFER) and a quantitative structure-activity relationship (QSAR) model to predict the log K_PDMS-w values for a large dataset of 290 organic chemicals from 11 diverse classes. For the pp-LFER model, E (excess molar refractivity), A (molecular H-bond donor ability), V (McGowan volume), and B (the H-bond acceptor properties) were introduced as the main correlated variables. However, the obtained model is much limited in terms of acquiring available descriptors. For this reason, we developed a QSAR model, and CrippenLogP (Crippen octanol-water partition coefficient), RNCG (Relative negative charge-most negative charge/total negative charge), ATSC4e (Centered Broto-Moreau autocorrelation-lag4/weighted by Sanderson electronegativities) and GATS6p (Geary autocorrelation-lag6/weighted by polarizabilities) were selected as the significant parameters. The predictive power and functional reliability of the presented models were confirmed with validation methods as described in previous studies. The adjusted determination coefficients (R²_adj) of 0.851 and 0.922 and leave-one-out cross-validated (Q²_LOO) of 0.841 and 0.907 revealed that the models have good predictive power and generalizability. Thus, the proposed models are simple yet accurate tools for predicting the log K_PDMS-w values and providing new insights to further understand the adsorption mechanism of organic compounds.

The Structures and Bioactivities of Fatty Acid Synthase Inhibitors

Background:

Fatty Acid Synthase (FAS or FASN) is a vital enzyme which catalyzes the de novo synthesis of long chain fatty acids. A number of studies have recently been reported that FAS was combined targets for the discovery of anti-obesity and anti-cancer drugs. Great interest has been developed in finding novel FAS inhibitors, and result in more than 200 inhibitors being reported.

Methods:

The reported research literature about the FAS inhibitors was collected and analyzedsised through major databases including Web of Science, and PubMed. Then the chemical stractures, FAS inhibitory activities, and Structure-Activity Relationships (SAR) were summarized focused on all these reported FAS inhibitors.

Results:

The 248 FAS inhibitors, which were reported during the past 20 years, could be divided into thiolactone, butyrolactone and butyrolactam, polyphenols, alkaloids, terpenoids, and other structures, in view of their structure characteristics. And the SAR of high inhibitory structures of each type was proposed in this paper.

Conclusion:

A series of synthetic quinolinone derivatives show strongest inhibitory activity in the reported FAS inhibitors. Natural polyphenols, existing in food and herbs, show more adaptive in medicine exploration because of their safety and efficiency. Moreover, screening the FAS inhibitors from microorganism and marine natural products could be the hot research directions in the future.

Recent advances in targeting the fatty acid biosynthetic pathway using fatty acid synthase inhibitors

INTRODUCTION

Elevated lipogenesis has been associated with a variety of diseases including obesity, cancer and nonalcoholic fatty liver disease (NAFLD). Fatty acid synthase (FASN) plays a pivotal role in de novo lipogenesis, making this multi-catalytic protein an attractive target for therapeutic intervention. Recently, the first FASN inhibitor successfully advanced through the drug development process and entered clinical evaluation in oncology. Areas covered: This review discusses the biological roles of FASN in three prominent disease areas: cancer, obesity-related disorders and NAFLD. Recent advances in drug discovery strategies and design of newer FASN inhibitors are also highlighted. Expert opinion: Despite the abundance of evidence linking the lipogenic pathway to cancer, progression of FASN-targeted molecules has been rather slow and challenging and no compounds have moved past the preclinical phase. The landscape has recently changed with the recent advancement of the first FASN inhibitor into clinical evaluation for solid tumors. Needless to say, the successful translation into the clinical setting will open opportunities for expanding the therapeutic utility of FASN inhibitors not just in oncology but in other diseases associated with elevated lipogenesis such as obesity, type 2 diabetes, and NAFLD.

In silico investigation of agonist activity of a structurally diverse set of drugs to hPXR using HM-BSM and HM-PNN

The human pregnane X receptor (hPXR) plays a critical role in the metabolism, transport and clearance of xenobiotics in the liver and intestine. The hPXR can be activated by a structurally diverse of drugs to initiate clinically relevant drug-drug interactions. In this article, in silico investigation was performed on a structurally diverse set of drugs to identify critical structural features greatly related to their agonist activity towards hPXR. Heuristic method (HM)-Best Subset Modeling (BSM) and HM-Polynomial Neural Networks (PNN) were utilized to develop the linear and non-linear quantitative structure-activity relationship models. The applicability domain (AD) of the models was assessed by Williams plot. Statistically reliable models with good predictive power and explain were achieved (for HM-BSM, r (2)=0.881, q LOO (2) =0.797, q EXT (2) =0.674; for HM-PNN, r (2)=0.882, q LOO (2) =0.856, q EXT (2) =0.655). The developed models indicated that molecular aromatic and electric property, molecular weight and complexity may govern agonist activity of a structurally diverse set of drugs to hPXR.

Phosphorhydrazide inhibitors: toxicological profile and antimicrobial evaluation assay, molecular modeling and QSAR study

A series of 23 novel phosphorhydrazide derivatives were synthesized and characterized by spectral techniques, and their anti-ChE, antibacterial and insecticide activities were investigated.

Synthesis and crystal structure of new temephos analogues as cholinesterase inhibitor: molecular docking, QSAR study, and hydrogen bonding analysis of solid state

A series of temephos (Tem) derivatives were synthesized and characterized by 31P, 13C, and 1H NMR and FT-IR spectral techniques. Also, the crystal structure of compound 9 was investigated. The hydrogen bonding energies (E2) were calculated by NBO analysis of the crystal cluster. The activities and the mixed-type mechanism of Tem derivatives were evaluated using the modified Ellman's and Lineweaver-Burk's methods on cholinesterase (ChE) enzymes. The inhibitory activities of Tem derivatives with a P═S moiety were higher than those with a P═O moiety. Docking analysis disclosed that the hydrogen bonds occurred between the OR (R=CH3 and C2H5) oxygen and N-H nitrogen atoms of the selected compounds and the receptor site (GLN and GLU) of ChEs. PCA-QSAR indicated that the correlation coefficients of the electronic variables were dominant compared to the structural descriptors. MLR-QSAR models clarified that the net charges of nitrogen and phosphorus atoms contribute important electronic function in the inhibition of ChEs. The validity of the QSAR model was confirmed by a LOO cross-validation method with q2=0.965 between the training and testing sets.

Molecular docking and QSAR studies: noncovalent interaction between acephate analogous and the receptor site of human acetylcholinesterase

Twelve new compounds of acephate (Ace) analogues were synthesized and characterized by (31)P, (13)C, and (1)H NMR and IR spectroscopy. The probable insecticide potential of these compounds as well as 23 previously prepared molecules with a general skeleton of RC(O)-NH-P(O)X1X2 was predicted by PASS software. Docking analysis showed that hydrophobic interaction and hydrogen bonding were created between the functional groups of Ace derivatives and the receptor sites of acetylcholinesterase. PCA-QSAR indicated that the electronic descriptors are dominated in comparison with the structural descriptors. The experimental-QSAR (R(2) = 0.903 and VIF < 2.997) and DFT-QSAR (R(2) = 0.990 and VIF ≤ 10) models clarified that the net charge of functional groups contributes an important function in an inhibition mechanism. Validity and integrity of this model were confirmed by the LOO cross-validation method with q(2) = 0.940 and low residuals between the training and testing sets. The correlation matrix of DFT-QSAR model confirmed the molecular docking results.

Four-dimensional structure-activity relationship model to predict HIV-1 integrase strand transfer inhibition using LQTA-QSAR methodology

Despite highly active antiretroviral therapy (HAART) implementation, there is a continuous need to search for new anti-HIV agents. HIV-1 integrase (HIV-1 IN) is a recently validated biological target for AIDS therapy. In this work, a four-dimensional quantitative structure-activity relationship (4D-QSAR) study using the new methodology named LQTA-QSAR approach with a training set of 85 HIV-1 IN strand transfer inhibitors (INSTI), containing the β-diketo acid (DKA) substructure, was carried out. The GROMACS molecular dynamic package was used to obtain a conformational ensemble profile (CEP) and LQTA-QSAR was employed to calculate Coulomb and Lennard-Jones potentials and to generate the field descriptors. The partial least-squares (PLS) regression model using 14 field descriptors and 8 latent variables (LV) yielded satisfactory statistics (R2= 0.897, SEC = 0.270, and F = 72.827), good performance in internal (QLOO2 = 0.842 and SEV = 0.314) and external prediction (Rpred2 = 0.839, SEP = 0.384, AREpred = 4.942%, k = 0.981, k′ = 1.016, and |R02 – R0′2 = 0.0257). The QSAR model was shown to be robust (leave-N-out cross validation; average QLNO2 = 0.834) and was not built by chance (y-randomization test; R2 intercept = 0.109; Q2 intercept = -0.398). Fair chemical interpretation of the model could be traced, including descriptors related to interaction with the metallic cofactors and the hydrophobic loop. The model obtained has a good potential for aid in the design of new INSTI, and it is a successful example of application of LQTA-QSAR as an useful tool to be used in computer-aided drug design (CADD).

A QSAR Study of Matrix Metalloproteinases Type 2 (MMP-2) Inhibitors with Cinnamoyl Pyrrolidine Derivatives

A multivariate PLS-QSAR study with a data set of 31 cinnamoyl pyrrolidine derivatives described as type 2 matrix metalloproteinases (MMP-2) inhibitors is presented in this paper. The variable selection was performed with the Ordered Predictors Selection (OPS) algorithm. The PLS model presented six descriptors and three Latent Variables (LV) that cumulated 71.845% of variance. Leave-N-out (LNO) cross validation and y-randomization tests showed that the model presented robustness and no chance correlation, respectively. The descriptors indicated that MMP-2 inhibition depends mainly on the electronic properties of the compounds. The model obtained can be useful as a support tool in the design of new MMP-2 inhibitors.

A new quantitative structure-property relationship model to predict bioconcentration factors of polychlorinated biphenyls (PCBs) in fishes using E-state index and topological descriptors

A quantitative structure-property relationship (QSPR) study for predicting the logarithm of bioconcentration factors (LogBCF) of polychlorinated biphenyls (PCBs) is presented in this work. For this, the descriptors were obtained using only the Simplified Molecular Input Line Entry System (SMILES) strings in the free web server Parameter Client. The model was built using the Partial Least Squares (PLS) regression method. The best model presented five descriptors (one E-state index and four topological descriptors) and a high quality for fit, internal, and external predictions. The leave-N-out (LNO) cross validation and the y-randomization test showed the model is robust and has no shown chance correlation. With a second test set, the model was compared to other models and presented a root mean square error (RMSE) very close to the best model. The mechanistic interpretation was corroborated by other works in the literature and by the descriptors' theory. Thus, the results meet the five Organization for Economic Co-operation and Development (OECD) principles for validation of QSA(P)R models, and it is expected the model can effectively predict the BCF values in fishes of the PCB congeners without highly reliable experimental BCF.

Reviewing ligand-based rational drug design: the search for an ATP synthase inhibitor

Following major advances in the field of medicinal chemistry, novel drugs can now be designed systematically, instead of relying on old trial and error approaches. Current drug design strategies can be classified as being either ligand- or structure-based depending on the design process. In this paper, by describing the search for an ATP synthase inhibitor, we review two frequently used approaches in ligand-based drug design: The pharmacophore model and the quantitative structure-activity relationship (QSAR) method. Moreover, since ATP synthase ligands are potentially useful drugs in cancer therapy, pharmacophore models were constructed to pave the way for novel inhibitor designs.