Generated QSAR Model Research Articles

QSAR/ANN approaches and molecular docking applied to calcium channel blockers

Artificial neural networks (ANN) are very useful for predicting biological activities in QSAR studies. ANNs allow the study of complex and nonlinear SAR. We use ANN and MLR methods to generate QSAR models for Calcium Channel Blockers activity of a series of 1,4-dihydropyridines. Molecular descriptors were calculated by using DFT method at the B3LYP/6-31G+ (d, p) level. Statistical analyzes show that the predicted values of the activities are in excellent agreement with the experimental results. Molecular docking studies have been performed, in order to re-estimate the activity of molecules as CCBs by analyzing their binding energies and mutual interaction types.

2,4,6-Trimethoxy chalcone derivatives: an integrated study for redesigning novel chemical entities as anticancer agents through QSAR, molecular docking, ADMET prediction, and computational simulation

QSAR, an efficient and successful approach for optimizing lead compounds in drug design, was employed to study a reported series of compounds derived from 2,4,6-trimethoxy chalcone derivatives. The ability of these compounds to inhibit CDK1 was examined, with the help of QSARINS software for model development. The generated QSAR model revealed three significant descriptors, exhibiting strong correlations with impressive statistical values: cross-validation leave-one-out correlation coefficient (Q 2LOO) = 0.6663, coefficient of determination (R 2) = 0.7863, external validation coefficient (R 2 ext) = 0.7854, cross-validation leave-many-out correlation coefficient (Q 2LMO) = 0.6256, Concordance Correlation Coefficient for cross-validation (CCCcv) = 0.8150, CCCtr = 0.8804, and CCCext = 0.8750. From the key structural findings and the insights gained from the descriptors, ETA_dPsi_A, WTPT-5, and GATS7s, new lead molecules were designed. The designed molecules were then evaluated for their CDK1 inhibitory activity using the three-descriptor model developed in this study. To evaluate their drug likeliness, in-silico ADMET predictions were made using Schrodinger’s Software. Molecular docking was carried out to determine the interactions of designed compounds with the target protein. The designed compounds having excellent binding pocket molecular stability and anticancer effectiveness was substantiated by the findings of the molecular dynamics simulation. The results of this work point out important properties and crucial interactions necessary for efficient protein inhibition, suggesting lead candidates for further development as novel anticancer agents. Communicated by Ramaswamy H. Sarma

In-silico studies of 2-aminothiazole derivatives as anticancer agents by QSAR, molecular docking, MD simulation and MM-GBSA approaches

Targeting Hec1/Nek2 is considered as crucial target for cancer treatment due to its significant role in cell proliferation. In pursuit of this, a series of twenty-five 2-aminothiazoles derivatives, along with their Hec1/Nek2 inhibitory activities were subjected to QSAR studies utilizing QSARINS software. The significant three descriptor QSAR model was generated, showing noteworthy statistical parameters: a correlation coefficient of cross validation leave one out (Q2 LOO) = 0.7965, coefficient of determination (R2) = 0.8436, (R2ext) = 0.6308, cross validation leave many out (Q2LMO) = 0.7656, Concordance Correlation Coefficient (CCCCV = 0.8875), CCCtr = 0.9151, and CCCext = 0.0.7241. The descriptors integral to generated QSAR model include Moreau-Broto autocorrelation, which represents the spatial autocorrelation of a property along the molecular graph’s topological structure (ATSC1i), Moran autocorrelation at lag 8, which is weighted by charges (MATS8c) and RPSA representing the total molecular surface area. It was noted that these descriptors significantly influence Hec1/Nek2 inhibitory activity of 2-aminothiazoles derivatives. New lead molecules were designed and predicted for their Hec1/Nek2 inhibitory activity based on the developed three descriptor model. Further, the ADMET and Molecular docking studies were carried out for these designed molecules. The three molecules were selected based on their docking score and further subjected for MD simulation studies. Post-MD MM-GBSA analysis were also performed to predicted the free binding energies of molecules. The study helped us to understand the key interactions between 2-aminothiazoles derivatives and Hec1/Nek2 protein that may be necessary to develop new lead molecules against cancer. Communicated by Ramaswamy H. Sarma

Probing the molecular mechanisms of α-synuclein inhibitors unveils promising natural candidates through machine-learning QSAR, pharmacophore modeling, and molecular dynamics simulations.

Parkinson's disease is characterized by a multifactorial nature that is linked to different pathways. Among them, the abnormal deposition and accumulation of α-synuclein fibrils is considered a neuropathological hallmark of Parkinson's disease. Several synthetic and natural compounds have been tested for their potency to inhibit the aggregation of α-synuclein. However, the molecular mechanisms responsible for the potency of these drugs to further rationalize their development and optimization are yet to be determined. To enhance our understanding of the structural requirements necessary for modulating the aggregation of α-synuclein fibrils, we retrieved a large dataset of α-synuclein inhibitors with their reported potency from the ChEMBL database to explore their chemical space and to generate QSAR models for predicting new bioactive compounds. The best performing QSAR model was applied to the LOTUS natural products database to screen for potential α-synuclein inhibitors followed by a pharmacophore design using the representative compounds sampled from each cluster in the ChEMBL dataset. Five natural products were retained after molecular docking studies displaying a binding affinity of - 6.0kcal/mol or lower. ADMET analysis revealed satisfactory properties and predicted that all the compounds can cross the blood-brain barrier and reach their target. Finally, molecular dynamics simulations demonstrated the superior stability of LTS0078917 compared to the clinical candidate, Anle138b. We found that LTS0078917 shows promise in stabilizing the α-synuclein monomer by specifically binding to its hairpin-like coil within the N-terminal region. Our dynamic analysis of the inhibitor-monomer complex revealed a tendency towards a more compact conformation, potentially reducing the likelihood of adopting an elongated structure that favors the formation and aggregation of pathological oligomers. These findings offer valuable insights for the development of novel α-synuclein inhibitors derived from natural sources.

Relevance of Machine Learning to Predict the Inhibitory Activity of Small Thiazole Chemicals on Estrogen Receptor.

Drug discovery requires the use of hybrid technologies for the discovery of new chemical substances. One of those interesting strategies is QSAR via applying an artificial intelligence system that effectively predicts how chemical alterations can impact biological activity via in-silico. Our present study aimed to work on a trending machine learning approach with a new opensource data analysis python script for the discovery of anticancer lead via building the QSAR model by using 53 compounds of thiazole derivatives. A python script has been executed with 53 small thiazole chemicals using Google collaboratory interface. A total of 82 CDK molecular descriptors were downloaded from "chemdes" web server and used for our study. After training the model, we checked the model performance via cross-validation of the external test set. The generated QSAR model afforded the ordinary least squares (OLS) regression as R2 = 0.542, F=8.773, and adjusted R2 (Q2) =0.481, std. error = 0.061, reg.coef_ developed were of, - 0.00064 (PC1), -0.07753 (PC2), -0.09078 (PC3), -0.08986 (PC4), 0.05044 (PC5), and reg.intercept_ of 4.79279 developed through stats models, formula module. The performance of test set prediction was done by multiple linear regression, support vector machine, and partial least square regression classifiers of sklearn module, which generated the model score of 0.5424, 0.6422 and 0.6422 respectively. Hence, we conclude that the R2values (i.e. the model score) obtained using this script via three diverse algorithms were correlated well and there is not much difference between them and may be useful in the design of a similar group of thiazole derivatives as anticancer agents.

Reliable CA-(Q)SAR generation based on entropy weight optimized by grid search and correction factors

Chromosome aberration (CA) is a serious genotoxicity of a compound, leading to carcinogenicity and developmental side effects. In the present manuscript, we developed a QSAR model for CA prediction using artificial intelligence methodologies. The reliable QSAR model was constructed based on an enlarged data set of 3208 compounds by optimizing machine learning and deep learning algorithms based on hyperparametric iterations and using multiple descriptors of molecular fingerprint in combination with drug-like molecular properties (MP) screened by entropy weight methodology on the open-source Python platform. Furthermore, molecular similarity for returning search and molecular connection index for additional descriptor were additionally introduced to differentiate the compounds with high similarity for correct CA prediction for QSAR model generation. The final generated CA-(Q)SAR model exhibited good prediction accuracy of 80.6%. The bias of the final model is about 0.9793. On the basis of generated QSAR model, data analyses were further performed to analyze the typical structure features in numerical intervals (MPI) of molecular properties MW, XlogP, and TPSA, respectively, for potential CA or non-CA toxicity with a normalized occurrence probability (NOP) more than 70%, which may provide useful clues for drug design of leads or candidate devoid of CA genotoxicity.

SMILES-Based QSAR and Molecular Docking Study of Oseltamivir Derivatives as Influenza Inhibitors

The quantitative structure–activity relationship studies for the modeling the activity of 72 oseltamivir derivatives as influenza neuraminidase (H1N1) inhibitors are performed using the Monte Carlo method based on the target function involving index of ideality of correlation (IIC). The optimal descriptors based on the combination of SMILES and hydrogen suppressor graphs (HSG) are employed for the model construction. Internal and external validation confirms robustness and good predictive power of the generated QSAR models. Identification of the activity-enhancing attributes indicates the positive impact of nitrogen and double bond on the influenza inhibitory activity. Finally, the pIC50 of the twelve new oseltamivir derivatives from ChEMBL database were predicted based on the proposed model. The new compounds showed high predicted pIC50 values and their molecular docking study was also investigated.

Combined machine learning and pharmacophore based virtual screening approaches to screen for antibiofilm inhibitors targeting LasR of Pseudomonas aeruginosa

Pseudomonas aeruginosa, a virulent pathogen affects patients with cystic fibrosis and nosocomial infections. Quorum sensing (QS) mechanism plays a crucial role in causing these ailments by mediating biofilm formation and expressing virulent genes. A novel approach to circumvent this bacterial infection is by hindering its QS network. Targeting LasR of las system serves beneficial as it holds the top position in QS system cascade. Here, we have integrated machine learning, pharmacophore based virtual screening, molecular docking and simulation studies to look for new leads as inhibitors for LasR. Support vector machine (SVM) learning algorithm was used to generate QSAR models from 66 antagonist dataset. The top three models resulted in correlation coefficient (R2) values of 0.67, 0.86, and 0.91, respectively. The correlation coefficient (R2 test) values on external test set were found to be 0.62, 0.57, and 0.55, respectively. A four-point pharmacophore model was developed. The pharmacophore hypothesis AAAD_1 was used to screen for potential leads against MolPort database in ZincPharmer. The leads which showed predicted pIC50 value of >8.00 by SVM models were subjected to docking analysis that reranked the compounds based on docking scores. Four top leads namely ZINC3851967 N-[3,5-bis(trifluoromethyl)phenyl]-5-tert-butyl-6-chloropyrazine-2-carboxamide, ZINC4024175 4-Amino-1-[(2R,3S,4S,5S)-3,4-dihydroxy-5-(hydroxymethyl)oxolan-2-yl]-2-oxopyrimidine-5-carbonitrile, ZINC2125703 N-[(5-Methoxy-4,7-dimethyl-2-oxo-2H-chromen-3-yl)acetyl]-beta-alanine, and ZINC3851966 N-[3,5-Bis(trifluoromethyl)phenyl]5-tert-butylpyrazine-2-carboxamide were selected. These compounds were checked for its stability by performing a molecular dynamics simulation for a period of 100 ns. The ADME properties of the leads were also determined. Hence, the compounds identified in this study can be used as possible leads for developing a novel inhibitor for LasR. Communicated by Ramaswamy H. Sarma

MACHINE LEARNING ALGORITHM USED TO BUILD A QSAR MODEL FOR PYRAZOLINE SCAFFOLD AS ANTI-TUBERCULAR AGENT

Machine learning has become an essential tool for drug research to generate pertinent structural information to design drugs with higher biological activities. In this paper, we used python program language on pyrazoline scaffold, which is collected from diverse literature for the inhibition of Mycobacterium tuberculosis. Pyrazoline, a small molecule scaffold could block the biosynthesis of mycolic acids, resulting in mycobacteria death and leading to anti-tubercular drug discovery. The generated QSAR model afforded the ordinary least squares (OLS) regression as R2 = 0.380, F=4.909, and Q2 =0.303, reg. coef_ developed were of 0.00651593 (molecular weight), -0.0069445 (hydrogen bond acceptor), - 0.07576775 (hydrogen bond donor), -0.239021 (LogP) and reg. intercept of 3.10331589018553 developed through statsmodels.formula module. The support vector machine of the sklearn module generated the model score of 0.6294242262068762, the developed model was cross-validated by using the test set compounds and plotting the linear curve between the predicted and actual pMIC50 value. We have found that the values obtained using this script correlated well and may be useful in the design of a similar group of pyrazoline analogs as anti-tubercular agents.

Polypharmacology of Gongronema latifolium leaf secondary metabolites against protein kinases implicated in Parkinson's disease and Alzheimer's disease

Neurodegenerative diseases (NDD) are one of the major health concerns around the globe; Parkinson's disease (PD) and Alzheimer's disease (AD) are the most prevalent form of NDD. Protein kinases (PKs), common protein domains implicated in neurodegenerative diseases , are regulatory protein of cell functions; and play a pivotal role in signal transduction. Hence, this study considered the computational screening of secondary metabolites form Gongronema latifolium leaf for their inhibitory activities against the three prevalent PKs leucine-rich repeat kinase 2 (LRRK2), GSK3β (glycogen kinase 3β) and MAPK14 associated with the onset of AD and PD. The compounds were initially screened against LRRK2, GSK3β and MAPK14 by molecular docking to retrieve the hits. Nine (9) compounds were chosen and calculated for binding free energy to determine their stability with the proteins. The Lipinski, Ghose, Veber and Egan rules were considered to predict the compounds drug-likeness, in addition to drug-drug interaction and GI absorption. 2D QSAR models were constructed using a Kernel-based PLS regression model with Canvas binary fingerprint as the X variable. From the selected nine-hit with favourable stability with the proteins, 4 flavonoids (Catechin, Gallocatechin, Butein and Isorhamnetin) showed drug-likeness attributes with a low tendency for drug-drug interaction and high GI absorption. The generated QSAR models also predicted moderate pIC50 values for these compounds against the three PKs. This study will help in future endeavours for finding effective therapy for AD and PD.

Improved 3D-QSAR Prediction by Multiple Conformational Alignments and Molecular Docking Studies to Design and Discover HIV-I Protease Inhibitors.

Inhibition of HIV-I protease enzyme is a strategic step for providing better treatment in retrovirus infections, which avoids resistance and possesses less toxicity. In the course of our research to discover new and potent protease inhibitors, 3D-QSAR (CoMFA and CoMSIA) models were generated using 3 different alignment techniques, including multifit alignment, docking based and Distill based alignment for 63 compounds. Novel molecules were designed from the output of this study. A total of 3 alignment methods were used to generate CoMFA and CoMSIA models. A Distill based alignment method was considered a better method according to different validation parameters. A 3D-QSAR model was generated and contour maps were discussed. The biological activity of designed molecules was predicted using the generated QSAR model to validate QSAR. The newly designed molecules were docked to predict binding affinity. In CoMFA, leave one out cross-validated coefficient (q2), conventional coefficient (r2) and predicted correlation coefficient (r2Predicted) values were found to be 0.721, 0.991 and 0.780, respectively. The best obtained CoMSIA model also showed significant cross-validated coefficient (q2), conventional coefficient (r2) and predicted correlation coefficient (r2Predicted) values of 0.714, 0.987 and 0.721, respectively. Steric and electrostatic contour maps generated from CoMFA and hydrophobic and hydrogen bond donor and hydrogen bond acceptor contour maps from CoMSIA models were used to design new and bioactive protease inhibitors by incorporating bioisosterism and knowledge-based structure-activity relationship. The results from both these approaches, ligand-based drug design and structure-based drug design, are adequate and promising to discover protease inhibitors.

In Silico Investigation: Opening Doors to Novel Thymidylate Synthase Inhibitors

Introduction: Lung cancer is presumed to be the most notable cause of morbidity and impermanency in human beings caused by uncontrolled cell proliferation of lung tissue which results in abrupt synthesis of DNA. Methods: Prevention of DNA synthesis can show distinctive effect on lung cancer by utilizing Thymidylate synthase (TS), a key rate-limiting enzyme in the DNA synthesis process. However, the available finite aggregate of clinically approved blockers and their corresponding side effects lead to the urgent origination of novel inhibitors. Results and Discussion: In silico approaches (QSAR and molecular docking) have been accomplished to discover new potential inhibitors of TS providing a new strategy to evolve novel thymidylate synthase inhibitors functional in lung cancer. Conclusion: In the present study chemical features of a series of compounds alongside their activities alternating over numerous orders of magnitudes was utilized to generate QSAR models, and these could be further employed to predict the activity of new designed compounds. 3D‒QSAR kNNSW based model with decent statistical data having q2 approximately 95% (internal validation) and 80% (external validation) has validated the importance of steric feature. Further docking analysis using D‒score and ligand receptor interactions indicated that all the studied compounds are well accommodated in the binding pocket of TS and disparities in the activity are controlled by hydrogen and hydrophobic interactions.

Pharmacophore modeling and 3D-QSAR studies of 2,4-disubstituted pyrimidine derivatives as Janus kinase 3 inhibitors

A robust pharmacophore model was developed and the structure-activity relationship was analyzed using 71 pyrimidine derivatives reported for covalent Janus Kinase 3 (JAK3) inhibition. Pharmacophore modeling developed a five featured pharmacophore: one H-bond acceptor, two H-bond donors, one hydrophobic, and one aromatic ring features. The atom-based three-dimensional QSAR models with statistical significance were generated using the training set of 52 compounds. The excellent predictive correlation coefficients were obtained for 3D models determined using a test set of 19 molecules. The generated QSAR model implies that the hydrophobic character is important for the JAK3 inhibitory activity of these compounds. Additionally, electron-withdrawing and hydrogen bond donor groups at specific positions positively contribute to the JAK3 inhibition potency. These results provided essential three-dimensional structural requirements and the crucial binding features of 2,4-disubstituted pyrimidine derivatives, which may direct for the design and discovery of novel potent JAK3 inhibitors.

Ligand-based pharmacophore filtering, atom based 3D-QSAR, virtual screening and ADME studies for the discovery of potential ck2 inhibitors

In the era of druggable genome, the assessment of the numerous molecular targets represents remarkable therapeutic opportunities in the pharmaceutical and chemical biology, simultaneously understanding the properties required for the small molecules to emerge as good drug candidate. Incorporation of readily amenable biological properties and pharmaceutical modulation is the core key for the small molecule driven target studies in cancer related disease, especially in the case of inhibitory type mechanisms. Among huge protein targets, CK2 which is a protein serine/threonine kinase, also called the “Predominant monitor” of the cell plays comprehensive role in the various cellular machinery pertaining to cell growth and cell death. Due to its ubiquitous nature and its activity to block its activity by small molecules resulting in favorably targeting prostate cancer, CK2 was identified to be the distinct element of our study. In this study, we invested rapid computational techniques to uncover the new CK2 inhibitors with promising pharmaceutical traits and advantages when matched with existing drugs. Initially, pharmacophore modeling and atom-based 3Dimensional-Quantitative structure activity relationship of 45 known CK2 inhibitors resulted in over few hundred hypotheses. The most excellent five point pharmacophore model (AAHHR) with two hydrogen bond acceptor (A), two hydrophobic groups (H), and one aromatic ring (R) was built. 3D-QSAR studies of the finest model yielded correlation co-efficient, R2 (0.9728) and Q2 (0.7965) for training and test set compounds respectively. Our effort of externally validating the generated QSAR model was quite momentous and encouraging with rm2 = 0.682, rcv2 = 0.779, k = 1.027 and r02 = 0.817 values points out the profoundness in predicting preeminence of model. The robust model was further employed as 3D query for virtual screening against ZINC database. The lead molecules were selected based on the fitness score, and then the lead molecules subsequently taken to molecular docking studies using Glide. Finally we identified six potential lead molecules were further subjected into ADME properties prediction by engaging Qikprop module. The ADME properties of six lead molecules ZINC15955420, Zinc13412605, Zinc40763681, Zinc40763677, Zinc26178676 and Zinc01536721 are under satisfactory range with desired ADME properties. On the whole, we believe our design of the new CK2 inhibitor serves as the approachable end resultant hits the can be researched further for clinical trials evaluation in prostate cancer and emphasize on choosing CK2 as the target that holds true protential for the genesis and proliferation of anti-CK2 agents to address prostate cancer therapeutics.

DNA Cleavage Properties, Antimicrobial and Cytotoxic Activity and 4D-QSAR Analysis of Some Pyrazole Derivatives

Background:An extensive study of 19 pyrazole derivatives were carried out based on the evaluation of DNA cleavage properties, antimicrobial and cytotoxic activities and 4D-QSAR analysis including pharmacophore modelling and bioactivity prediction by the Electron Conformational-Genetic Algorithm (EC-GA) method.Methods:The pyrazole derivatives were tested for their antimicrobial activity against certain human pathogenic organisms using the agar diffusion procedure. Binding of compounds with DNA was studied by gel electrophoresis using plasmid pBR322 DNA. The compounds were investigated for their properties as cytotoxic agents by brine shrimp lethality bioassay. To identify the pharmacophoric elements and find out the most important molecular properties which govern cytotoxic activity, multiple conformations of the compounds were used.Results:The urea derivatives of pyrazole had higher antibacterial activities against Gram-negative bacteria than against Gram-positive bacteria. Many of the compounds were found to cleave plasmid pBR322 DNA from the supercoiled form to the nicked circular. The cytotoxicity values of the compounds ranged from 13.87 to 84.1 µg/mL. The generated QSAR model was evaluated through the use of the Leave-One-Out Cross Validation (LOO-CV) method. A statistically significant and considerably predictive QSAR model was obtained with 4- descriptors resulting in R2 training =0.8223, R2 test =0.9346, q2=0.6201, q2 ext1=0.8672, q2 ext2= 0.8662 and q2 ext3=0.9511.Discussion:The generated model demonstrates that geometrical parameters are more correlated with cytotoxic activity. The resulting EC-GA model would provide benefits to design novel bioactive pyrazole derivatives which are more potent and have less side effects.Conclusion:It is believed that the generated QSAR model gives insight into developing new more potent pyrazole derivative drugs.

Naproxenylamino acid derivatives: Design, synthesis, docking, QSAR and anti-inflammatory and analgesic activity

This work aimed to design and synthesize a safe nonsteroidal anti-inflammatory drug NSAIDs agent based on Naproxen scaffold. The structure of compounds 6-21 established on the basis of different spectral data. Anti-inflammatory and analgesic profile were examined for synthesizing compounds. The compounds 6 and 17 have shown a higher anti-inflammatory potency than Naproxen. The compounds 16, 19 and 21 have exhibited the highest analgesic potency compared to other tested compounds. The synthesized compounds have shown negligible ulcerogenic effect and may be considered as safer drugs than naproxen for treating inflammatory conditions. The molecular docking against COX-2 was performed, it verified that compound 6, 17 show stronger interactions with COX-2. This may result in a better inhibitory effect on COX-2. The best generated QSAR model shows correlation between BCUT_SMR_3 and vsurf_Wp6 with biological activity. ADMET in silico showed that these compounds are a good oral bioavailability without observed carcinogenesis affect.

QSARpy: A new flexible algorithm to generate QSAR models based on dissimilarities. The log Kow case study

Several methods exist to develop QSAR models automatically. Some are based on indices of the presence of atoms, other on the most similar compounds, other on molecular descriptors. Here we introduce QSARpy v1.0, a new QSAR modeling tool based on a different approach: the dissimilarity. This tool fragments the molecules of the training set to extract fragments that can be associated to a difference in the property/activity value, called modulators. If the target molecule share part of the structure with a molecule of the training set and differences can be explained with one or more modulators, the property/activity value of the molecule of the training set is adjusted using the value associated to the modulator(s). This tool is tested here on the n-octanol/water partition coefficient (Kow, usually expressed in logarithmic units as log Kow). It is a key parameter in risk assessment since it is a measure of hydrophobicity. Its wide spread use makes these estimation methods very useful to reduce testing costs. Using QSARpy v1.0, we obtained a new model to predict log Kow with accurate performance (RMSE 0.43 and R2 0.94 for the external test set), comparing favorably with other programs. QSARpy is freely available on request.

DFT and 3D-QSAR Studies of Anti-Cancer Agents m-(4-Morpholinoquinazolin-2-yl) Benzamide Derivatives for Novel Compounds Design

As a group of diversified frameworks, quinazolin derivatives displayed a broad field of biological functions, especially as anticancer. To investigate the quantitative structure-activity relationship, 3D-QSAR models were generated with 24 quinazolin scaffold molecules. The experimental and predicted pIC50 values for both training and test set compounds showed good correlation, which proved the robustness and reliability of the generated QSAR models. The most effective CoMFA and CoMSIA were obtained with correlation coefficient r2 ncv of 1.00 (both) and leave-one-out coefficient q2 of 0.61 and 0.59, respectively. The predictive abilities of CoMFA and CoMSIA were quite good with the predictive correlation coefficients (r2 pred ) of 0.97 and 0.91. In addition, the statistic results of CoMFA and CoMSIA were used to design new quinazolin molecules.

On the relationship of anthranilic derivatives structure and the FXR (Farnesoid X receptor) agonist activity

Farnesoid X receptor (FXR) is a nuclear receptor related to lipid and glucose homeostasis and is considered an important molecular target to treatment of metabolic diseases as diabetes, dyslipidemia, and liver cancer. Nowadays, there are several FXR agonists reported in the literature and some of it in clinical trials for liver disorders. Herein, a compound series was employed to generate QSAR models to better understand the structural basis for FXR activation by anthranilic acid derivatives (AADs). Furthermore, here we evaluate the inclusion of the standard deviation (SD) of EC50 values in QSAR models quality. Comparison between the use of experimental variance plus average values in model construction with the standard method of model generation that considers only the average values was performed. 2D and 3D QSAR models based on the AAD data set including SD values showed similar molecular interpretation maps and quality (Q2LOO, Q2(F2), and Q2(F3)), when compared to models based only on average values. SD-based models revealed more accurate predictions for the set of test compounds, with lower mean absolute error indices as well as more residuals near zero. Additionally, the visual interpretation of different QSAR approaches agrees with experimental data, highlighting key elements for understanding the biological activity of AADs. The approach using standard deviation values may offer new possibilities for generating more accurate QSAR models based on available experimental data.

A receptor dependent-4D QSAR approach to predict the activity of mutated enzymes

Screening and selection tools to obtain focused libraries play a key role in successfully engineering enzymes of desired qualities. The quality of screening depends on efficient assays; however, a focused library generated with a priori information plays a major role in effectively identifying the right enzyme. As a proof of concept, for the first time, receptor dependent – 4D Quantitative Structure Activity Relationship (RD-4D-QSAR) has been implemented to predict kinetic properties of an enzyme. The novelty of this study is that the mutated enzymes also form a part of the training data set. The mutations were modeled in a serine protease and molecular dynamics simulations were conducted to derive enzyme-substrate (E-S) conformations. The E-S conformations were enclosed in a high resolution grid consisting of 156,250 grid points that stores interaction energies to generate QSAR models to predict the enzyme activity. The QSAR predictions showed similar results as reported in the kinetic studies with >80% specificity and >50% sensitivity revealing that the top ranked models unambiguously differentiated enzymes with high and low activity. The interaction energy descriptors of the best QSAR model were used to identify residues responsible for enzymatic activity and substrate specificity.