Predictive Quantitative Structure-activity Relationship Research Articles

Quantitative structure-activity relationship and molecular docking studies on human proteasome inhibitors for anticancer activity targeting NF-κB signaling pathway

The abnormal ubiquitin-proteasome is found as an important target in various human diseases, especially in cancer, and recently it has received prevalent attention as a challenging therapeutic target. The current work is designed to derive a predictive two-dimensional quantitative structure-activity relationship model for anticancer human proteasome target of NF-κB signaling pathway. The established 2 D-QSAR is dependent on multiple linear regression approach and validated through leave-One-Out and external test set prediction method. The robust QSAR model showed the r2 of 0.83 and q2 of 0.80 and pred_r2 of 0.77. Three chemical properties, electronegativity count, average potential, and T_2_N_6 were identified as significant descriptors to predict the anticancer activities of the proteasome antagonists. Besides, the predicted top hit compounds were considered to check out the compliance with Rule of five and pharmacokinetic parameters for oral bioavailability in the human body. The molecular docking was accomplished to unravel the molecular mode of action of best-predicted compounds which was compatible with the standard drug. Following this approach, lastly two compounds NP and AP were recognized as the best candidates since these top compounds follow all the standard limit point of entire filters and indicated effective and decent docking score. The outcomes of the study sturdily suggested that the developed model and top hit compound’s binding conformation are rational in the exploration of unknown antagonist’s anticancer activity. The research would be of great support and is supposed to be of immense significance in the development and designing of drug-like candidates in preliminary drug discovery. Communicated by Ramaswamy H. Sarma

4D- quantitative structure–activity relationship modeling: making a comeback

ABSTRACT Introduction: Predictive Quantitative Structure–Activity Relationship (QSAR) modeling has become an essential methodology for rapidly assessing various properties of chemicals. The vast majority of these QSAR models utilize numerical descriptors derived from the two- and/or three-dimensional structures of molecules. However, the conformation-dependent characteristics of flexible molecules and their dynamic interactions with biological target(s) is/are not encoded by these descriptors, leading to limited prediction performances and reduced interpretability. 2D/3D QSAR models are successful for virtual screening, but typically suffer at lead optimization stages. That is why conformation-dependent 4D-QSAR modeling methods were developed two decades ago. However, these methods have always suffered from the associated computational cost. Recently, 4D-QSAR has been experiencing a significant come-back due to rapid advances in GPU-accelerated molecular dynamic simulations and modern machine learning techniques. Areas covered: Herein, the authors briefly review the literature regarding 4D-QSAR modeling and describe its modern workflow called MD-QSAR. Challenges and current limitations are also highlighted. Expert opinion: The development of hyper-predictive MD-QSAR models could represent a disruptive technology for analyzing, understanding, and optimizing dynamic protein-ligand interactions with countless applications for drug discovery and chemical toxicity assessment. Therefore, there has never been a better time and relevance for molecular modeling teams to engage in hyper-predictive MD-QSAR modeling.

Consensus QSPR modelling for the prediction of cellular response and fibrinogen adsorption to the surface of polymeric biomaterials

ABSTRACTIn the current study, we have developed predictive quantitative structure–activity relationship (QSAR) models for cellular response (foetal rate lung fibroblast proliferation) and protein adsorption (fibrinogen adsorption (FA)) on the surface of tyrosine-derived biodegradable polymers designed for tissue engineering purpose using a dataset of 66 and 40 biodegradable polymers, respectively, employing two-dimensional molecular descriptors. Best four individual models have been selected for each of the endpoints. These models are developed using partial least squares regression with a unique combination of six and four descriptors for cellular response and protein adsorption, respectively. The generated models were strictly validated using internal and external metrics to determine the predictive ability and robustness of proposed models. Subsequently, the validated individual models for each response endpoints were used for the generation of ‘intelligent’ consensus models (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) to improve the quality of predictions for the external data set. These models may help in prediction of virtual polymer libraries for rational design/optimization for properties relevant to biomedical applications prior to their synthesis.

Identification and description of the uncertainty, variability, bias and influence in quantitative structure-activity relationships (QSARs) for toxicity prediction

Improving regulatory confidence in, and acceptance of, a prediction of toxicity from a quantitative structure-activity relationship (QSAR) requires assessment of its uncertainty and determination of whether the uncertainty is acceptable. Thus, it is crucial to identify potential uncertainties fundamental to QSAR predictions. Based on expert review, sources of uncertainties, variabilities and biases, as well as areas of influence in QSARs for toxicity prediction were established. These were grouped into three thematic areas: uncertainties, variabilities, potential biases and influences associated with 1) the creation of the QSAR, 2) the description of the QSAR, and 3) the application of the QSAR, also showing barriers for their use. Each thematic area was divided into a total of 13 main areas of concern with 49 assessment criteria covering all aspects of QSAR development, documentation and use. Two case studies were undertaken on different types of QSARs that demonstrated the applicability of the assessment criteria to identify potential weaknesses in the use of a QSAR for a specific purpose such that they may be addressed and mitigation strategies can be proposed, as well as enabling an informed decision on the adequacy of the model in the considered context.

QSAR and molecular docking studies on 4-quinoline carboxylic acid derivatives as inhibition of vesicular stomatitis virus replication

The current study describes the development of in silico models based on quantitative structure-activity relationship (QSAR) analysis has been performed on 4-quinoline carboxylic acid derivatives as inhibition capacity of vesicular stomatitis virus replication in Madin Darby canine kidney epithelial cells. A highly descriptive and predictive QSAR model was obtained through the calculation of alignment-independent descriptors using MOE 2009.10 software. For a training set of 20 compounds, the partial least squares analyses result in a model which displays a squared correlation coefficient (r2) of 0.913. Validation of this model was performed using leave-one-out (q2) of 0.842. This model gives (r2pre) of 0.889 for a test set of five compounds. Docking studies were performed for 25 compounds to investigate the mode of interaction between 4-quinoline carboxylic acid derivatives and the active site of the human dihydroorotate dehydrogenase.

Micropollutant Oxidation Studied by Quantum Chemical Computations: Methodology and Applications to Thermodynamics, Kinetics, and Reaction Mechanisms.

The abatement of organic micropollutants during oxidation processes has become an emerging issue for various urban water systems such as drinking water, wastewater, and water reuse. Reaction kinetics and mechanisms play an important role in terms of efficiency of these processes and the formation of transformation products, which are controlled by functional groups in the micropollutants and the applied oxidants. So far, the kinetic and mechanistic information on the underlying reactions was obtained by experimental studies; additionally, predictive quantitative structure-activity relationships (QSARs) were applied to determine reaction kinetics for the oxidation of emerging compounds. Since this experimental approach is very laborious and there are tens of thousands potential contaminants, alternative strategies need to be developed to predict the fate of micropollutants during oxidative water treatment. Due to significant developments in quantum chemical (QC) computations in recent years and increased computational capacity, QC-based methods have become an alternative or a supplement to the current experimental approach. This Account provides a critical assessment of the current state-of-the-art of QC-based methods for the assessment of oxidation of micropollutants. Starting from a given input structure, QC computations need to locate energetic minima on the potential energy surface (PES). Then, useful thermodynamic and kinetic information can be estimated by different approaches: Experimentally determined reaction mechanisms can be validated by identification of transition structures on the PES, which can be obtained for addition reactions, heavy atom transfer (Cl+, Br+, O·) and H atom transfer (simultaneous proton and electron transfer) reactions. However, transition structures in the PES cannot be obtained for e--transfer reactions. Second-order rate constants k for the reactions of micropollutants with chemical oxidants can be obtained by ab initio calculations or by QSARs with various QC descriptors. It has been demonstrated that second-order rate constants from ab initio calculations are within factors 3-750 of the measured values, whereas QSAR-based methods can achieve factors 2-4 compared to the experimental data. The orbital eigenvalue of the highest occupied molecular orbital ( EHOMO) is the most commonly used descriptor for QSAR-based computations of k-values. In combination with results from experimental studies, QC computations can also be applied to investigate reaction mechanisms for verification/understanding of oxidative mechanisms, calculation of branching ratios or regioselectivity, evaluation of the experimental product distribution and assessment of substitution effects. Furthermore, other important physical-chemical constants such as unknown equilibria for species, which are not measurable due to low concentrations, or p Ka values of reactive transient species can be estimated. With further development of QC-based methods, it will become possible to implement kinetic and mechanistic information from such computations in in silico models to predict oxidative transformation of micropollutants. Such predictions can then be complemented by tailored experimental studies to confirm/falsify the computations.

Dissecting Machine-Learning Prediction of Molecular Activity: Is an Applicability Domain Needed for Quantitative Structure-Activity Relationship Models Based on Deep Neural Networks?

Deep neural networks (DNNs) are the major drivers of recent progress in artificial intelligence. They have emerged as the machine-learning method of choice in solving image and speech recognition problems, and their potential has raised the expectation of similar breakthroughs in other fields of study. In this work, we compared three machine-learning methods-DNN, random forest (a popular conventional method), and variable nearest neighbor (arguably the simplest method)-in their ability to predict the molecular activities of 21 in vivo and in vitro data sets. Surprisingly, the overall performance of the three methods was similar. For molecules with structurally close near neighbors in the training sets, all methods gave reliable predictions, whereas for molecules increasingly dissimilar to the training molecules, all three methods gave progressively poorer predictions. For molecules sharing little to no structural similarity with the training molecules, all three methods gave a nearly constant value-approximately the average activity of all training molecules-as their predictions. The results confirm conclusions deduced from analyzing molecular applicability domains for accurate predictions, i.e., the most important determinant of the accuracy of predicting a molecule is its similarity to the training samples. This highlights the fact that even in the age of deep learning, developing a truly high-quality model relies less on the choice of machine-learning approach and more on the availability of experimental efforts to generate sufficient training data of structurally diverse compounds. The results also indicate that the distance to training molecules offers a natural and intuitive basis for defining applicability domains to flag reliable and unreliable quantitative structure-activity relationship predictions.

Chemistry-Wide Association Studies (CWAS): A Novel Framework for Identifying and Interpreting Structure-Activity Relationships.

Quantitative structure-activity relationships (QSAR) models are often seen as a "black box" because they are considered difficult to interpret. Meanwhile, qualitative approaches, e.g., structural alerts (SA) or read-across, provide mechanistic insight, which is preferred for regulatory purposes, but predictive accuracy of such approaches is often low. Herein, we introduce the chemistry-wide association study (CWAS) approach, a novel framework that both addresses such deficiencies and combines advantages of statistical QSAR and alert-based approaches. The CWAS framework consists of the following steps: (i) QSAR model building for an end point of interest, (ii) identification of key chemical features, (iii) determination of communities of such features disproportionately co-occurring more frequently in the active than in the inactive class, and (iv) assembling these communities to form larger (and not necessarily chemically connected) novel structural alerts with high specificity. As a proof-of-concept, we have applied CWAS to model Ames mutagenicity and Stevens-Johnson Syndrome (SJS). For the well-studied Ames mutagenicity data set, we identified 76 important individual fragments and assembled co-occurring fragments into SA both replicative of known as well as representing novel mutagenicity alerts. For the SJS data set, we identified 29 important fragments and assembled co-occurring communities into SA including both known and novel alerts. In summary, we demonstrate that CWAS provides a new framework to interpret predictive QSAR models and derive refined structural alerts for more effective design and safety assessment of drugs and drug candidates.

Role of Topological, Electronic, Geometrical, Constitutional and Quantum Chemical Based Descriptors in QSAR: mPGES-1 as a Case Study

Quantitative Structure Activity Relationship (QSAR) is one of the widely used ligand based drug design strategies. Although a number of QSAR studies have been reported, debates over the limitations and accuracy of QSAR models are at large. In this review the applicability of various classes of molecular descriptors in QSAR has been explained. Protocol for QSAR model development and validation is presented. Here we discuss a case study on 7-Phenyl-imidazoquinolin-4(5H)-one derivatives as potent mPGES-1 inhibitors to identify crucial physicochemical properties responsible for mPGES-1 inhibition. The case study explains the methodology for QSAR analysis, validation of the developed models and role of diverse classes of molecular descriptors in defining the inhibitory activity of considered inhibitors. Various molecular descriptors derived from 2D/3D structure and quantum mechanics were considered in the study. Initially, QSAR models for the training set compounds were developed individually for each class of molecular descriptors. Further, a combined QSAR model was developed using the best descriptor from all the classes. The models obtained were further validated using an external test set. Combined QSAR model exhibited the best correlation (r = 0.80) between the predicted and experimental biological activities of test set compounds. The results of the QSAR analysis were further backed by docking studies. From the results of the case study it is evident that rather than a single class of molecular descriptors, a combination of molecular descriptors belonging to different classes significantly improves the QSAR predictions. The techniques and protocol discussed in the present work might be of significant importance while developing QSAR models of various drug targets.

Optimal Piecewise Linear Regression Algorithm for QSAR Modelling.

Quantitative Structure-Activity Relationship (QSAR) models have been successfully applied to lead optimisation, virtual screening and other areas of drug discovery over the years. Recent studies, however, have focused on the development of models that are predictive but often not interpretable. In this article, we propose the application of a piecewise linear regression algorithm, OPLRAreg, to develop both predictive and interpretable QSAR models. The algorithm determines a feature to best separate the data into regions and identifies linear equations to predict the outcome variable in each region. A regularisation term is introduced to prevent overfitting problems and implicitly selects the most informative features. As OPLRAreg is based on mathematical programming, a flexible and transparent representation for optimisation problems, the algorithm also permits customised constraints to be easily added to the model. The proposed algorithm is presented as a more interpretable alternative to other commonly used machine learning algorithms and has shown comparable predictive accuracy to Random Forest, Support Vector Machine and Random Generalised Linear Model on tests with five QSAR data sets compiled from the ChEMBL database.

Design, Synthesis, Characterization, QSAR, Docking, Anti-inflammatory and Analgesic Evaluation of Some New Phthalazinediones.

phthalazine derivatives were reported to possess anticonvulsant , cardiotonic , antibacterial, analgesic , anti-inflammatory, and anti-microbial activity. In the current study, we applied the QSAR for prediction of newly phthalazinediones incorporating thioamide moiety aiming to reach a more potent anti-inflammatory and Analgesic agent. Phthalazinediones 10-15 have been synthesized through condensation of dibenzobarallene 3 with thiosemicarbazides 4-8. One equation was predicted using quantitative structure activity relationship (QSAR) and regression analysis for the anti-inflammatory activity with a regression correlation (R) close to unity. The docking studies were performed to investigate the biological trends of the organic compounds (thiol form) against cyclooxygenas- 2 enzyme, which is a responsible inflammation mediator by using Molgro Virtual Docker (MVD) software. The anti-inflammatory activity and analgesic effect of the thioamides 10-15 were determined by collagen II-adjuvant induced paw edema test in rats. Compounds 10, 11, 12, and 14, exhibited promising anti-inflammatory activity. Furthermore, in the pain scoring, compounds 10, 11 and 12 were found to be more effective than piroxicam and the order of the analgesic effect of the investigated compounds is as followed 14 >12 > 10 > 11 > 15. It is clear from the foregoing that the compound 14 is a promising compound if future pharmacological detailed studies. This is consistent with what has been predictable equation 1 in this study.

Robust QSAR prediction models for volume of distribution at steady state in humans using relative distance measurements

ABSTRACTThe building of quantitative structure–activity relationship (QSAR) models for the in silico prediction of volume distribution for drugs at steady-state levels is vital for the selection of potential drugs at the synthesis stage. Using molecular descriptor matrixes, some regression models presenting low accuracy have been proposed, mainly due to the difficulty of compiling an appropriate dataset and the lack of information on dataset representation. In this paper, we use a benchmark dataset of very diverse drugs for the development of predictive models for volume distribution based on the use of relative distance matrixes as the input data to QSAR algorithms. Support vector machine, complex tree, bagged tree and Gaussian process regression algorithms were tested for fingerprint, similarity and relative distance matrixes used as input data, and the results of the built models were compared. Relative distance matrixes generated robust regression models in the training and external validation stages performed using cross-validation, obtaining values for correlation coefficient, bias, slope and root-mean-square error close to the ideal. Relative distance matrixes were also used for the design of classification models, obtaining excellent results with values of accuracy and area under receiver operating characteristic (AUC) close to 100%.

General Approach to Estimate Error Bars for Quantitative Structure-Activity Relationship Predictions of Molecular Activity.

Key requirements for quantitative structure-activity relationship (QSAR) models to gain acceptance by regulatory authorities include a defined domain of applicability (DA) and appropriate measures of goodness-of-fit, robustness, and predictivity. Hence, many DA metrics have been developed over the past two decades. The most intuitive are perhaps distance-to-model metrics, which are most commonly defined in terms of the mean distance between a molecule and its k nearest training samples. Detailed evaluations have shown that the variance of predictions by an ensemble of QSAR models may serve as a DA metric and can outperform distance-to-model metrics. Intriguingly, the performance of ensemble variance metric has led researchers to conclude that the error of predicting a new molecule does not depend on the input descriptors or machine-learning methods but on its distance to the training molecules. This implies that the distance to training samples may serve as the basis for developing a high-performance DA metric. In this article, we introduce a new Tanimoto distance-based DA metric called the sum of distance-weighted contributions (SDC), which takes into account contributions from all molecules in a training set. Using four acute chemical toxicity data sets of varying sizes and four other molecular property data sets, we demonstrate that SDC correlates well with the prediction error for all data sets regardless of the machine-learning methods and molecular descriptors used to build the QSAR models. Using the acute toxicity data sets, we compared the distribution of prediction errors with respect to SDC, the mean distance to k-nearest training samples, and the variance of random forest predictions. The results showed that the correlation with the prediction error was highest for SDC. We also demonstrate that SDC allows for the development of robust root mean squared error (RMSE) models and makes it possible to not only give a QSAR prediction but also provide an individual RMSE estimate for each molecule. Because SDC does not depend on a specific machine-learning method, it represents a canonical measure that can be widely used to estimate individual molecule prediction errors for any machine-learning method.

Toxicokinetics of Crude Oil Components in Arctic Copepods.

The risk of accidental oil spills in the Arctic is on the rise due to increased shipping and oil exploration activities, making it essential to calibrate parameters for risk assessment of oil spills to Arctic conditions. The toxicokinetics of crude oil components were assessed by exposing one lipid-poor (CIII) and one lipid-rich (CV) stage of the Arctic copepod Calanus hyperboreus to crude oil WSF (water-soluble fraction). Water concentrations and total body residues (BR), as well as lipid volume fractions, were measured at regular intervals during exposure and recovery. Bioconcentration factors (BCFs) and elimination rates ( ke) for 26 petrogenic oil components were estimated from one-compartment models fitted to the BR data. Our parameters were compared to estimations made by the OMEGA bioaccumulation model, which uses the octanol-water partitioning coefficient ( KOW) in QSAR (quantitative structure-activity relationship) predictions. Our parameters for the lipid-poor CIIIs generally agreed with the OMEGA predictions, while neither the BCFs nor the kes for the lipid-rich CVs fitted within the realistic range of the OMEGA parameters. Both the uptake and elimination rates for the CVs were in general half an order of magnitude lower than the OMEGA predictions, showing an overestimation of these parameters by the OMEGA model.

QSAR Study on Antioxidant Tripeptides and the Antioxidant Activity of the Designed Tripeptides in Free Radical Systems.

In this study, quantitative structure-activity relationship (QSAR) models were determined based on 91 antioxidant tripeptides. We firstly adopted the stepwise regression (SWR) method for selecting key variables without autocorrelation and then utilized multiple linear regression (MLR), support vector machine (SVM), random forest (RF), and partial least square regression (PLS) to develop predictive QSAR models based on the screened variables. The results demonstrated that all the established models have good reliability (R2train > 0.86, Q2train > 0.70) and relatively good predictability (R2test > 0.88). The contribution of amino acid residues was calculated from the stepwise regression combined with multiple linear regression (SWR-MLR) method model that shows Trp, Tyr, or Cys at C-terminus is favorable for antioxidant activity of tripeptides. Nineteen antioxidant tripeptides were designed based on SWR-MLR models, and the antioxidant activity of these tripeptides were evaluated using three antioxidant assays in free radical systems (1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging capacity, trolox equivalent antioxidant capacity assay, and the ferric reducing antioxidant power assay). The experimental antioxidant activities of these tripeptides were higher than the calculated/predicted activity values of the QSAR models. The QSAR models established can be used to identify and screen novel antioxidant tripeptides with high activity.

Classical and 3D QSAR studies of larvicidal monoterpenes against Aedes aegypti: new molecular insights for the rational design of more active compounds

Monoterpenes are compounds found on essential oils of diverse plants, with biological activities such as repellent, insecticidal and larvicidal against some organism and microorganism species. Dengue, Chikungunya, and Zika are important public health problems caused by viruses, which are transmitted by the bite of Aedes aegypti mosquitoes. One of the most common strategies to combat these diseases is the control of the vector proliferation, by physical and chemical methods. Thus, the design, synthesis, and test of the compounds with larvicidal profile have emerged as an important research line. In this paper, we present the use of computational approaches to provide robust, predictive, and conveniently interpretable quantitative structure-activity relationships models, involving a series of 55 monoterpenes and structurally related compounds, which presented larvicidal activity against females of Aedes aegypti, expressed by IC50. Through the proposition and analysis of 2D and 3D molecular descriptors, consistent and easily understood molecular bases about larvicidal activity are provided, for the proposition of new molecules with more active profile. Theoretical geometry optimization of the 55 compounds was performed using the PM6 semiempirical Hamiltonian, followed by B3LYP/6-31+G(d) level of theory, using Gaussian 09W® package. A set of classical descriptors was obtained from Dragon® software for each compound and submitted to a variable selection procedure, using the Ordered Predictors Selector (OPS) algorithm, considering larvicidal (pIC50) activities previously reported. Then, a 3D quantitative structure-activity relationship (QSAR) investigation was performed by Open3DQSAR program, using the same values of activity, for the investigation of sterical and electrostatic molecular fields. For classical QSAR, five descriptors were selected as the most important, providing a robust and predictive QSAR model via multiple linear regression (MLR). In addition, a good 3D-QSAR model was found, with a clear interpretation of the physical sense of the fields, according to the classical model. Molecules with more elongated structures contain a six-membered ring with 1,4 substitution pattern, highest lipophilicity, less volume on a central moiety, and lateral hydrophobic chain at six-membered ring, tend to be more actives.

Effect of dissolved organic matter on pre-equilibrium passive sampling: A predictive QSAR modeling study

Pre-equilibrium passive sampling is a simple and promising technique for studying sampling kinetics, which is crucial to determine the distribution, transfer and fate of hydrophobic organic compounds (HOCs) in environmental water and organisms. Environmental water samples contain complex matrices that complicate the traditional calibration process for obtaining the accurate rate constants. This study proposed a QSAR model to predict the sampling rate constants of HOCs (polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and pesticides) in aqueous systems containing complex matrices. A homemade flow-through system was established to simulate an actual aqueous environment containing dissolved organic matter (DOM) i.e. humic acid (HA) and (2-Hydroxypropyl)-β-cyclodextrin (β-HPCD)), and to obtain the experimental rate constants. Then, a quantitative structure-activity relationship (QSAR) model using Genetic Algorithm-Multiple Linear Regression (GA-MLR) was found to correlate the experimental rate constants to the system state including physicochemical parameters of the HOCs and DOM which were calculated and selected as descriptors by Density Functional Theory (DFT) and Chem 3D. The experimental results showed that the rate constants significantly increased as the concentration of DOM increased, and the enhancement factors of 70-fold and 34-fold were observed for the HOCs in HA and β-HPCD, respectively. The established QSAR model was validated as credible (RAdj.2=0.862) and predictable (Q2=0.835) in estimating the rate constants of HOCs for complex aqueous sampling, and a probable mechanism was developed by comparison to the reported theoretical study. Main finding of the workThe present study established a QSAR model of passive sampling rate constants and calibrated the effect of DOM on the sampling kinetics.

Evaluation of the antioxidant properties of curcumin derivatives by genetic function algorithm

The prevalence of degenerative diseases in recent time has triggered extensive research on their control. This condition could be prevented if the body has an efficient antioxidant mechanism to scavenge the free radicals which are their main causes. Curcumin and its derivatives are widely employed as antioxidants. The free radical scavenging activities of curcumin and its derivatives have been explored in this research by the application of quantitative structure activity relationship (QSAR). The entire data set was optimized at the density functional theory (DFT) level using the Becke's three-parameter Lee-Yang-Parr hybrid functional (B3LYP) in combination with the 6-311G∗ basis set. The training set was subjected to QSAR studies by genetic function algorithm (GFA). Five predictive QSAR models were developed and statistically subjected to both internal and external validations. Also the applicability domain of the developed model was accessed by the leverage approach. Furthermore, the variation inflation factor, (VIF), mean effect (MF) and the degree of contribution (DC) of each descriptor in the resulting model were calculated. The developed models met all the standard requirements for acceptability upon validation with highly impressive results (R=0.965,R2=0.931,Q2(RCV2)=0.887,Rpred2=0.844,cRp2=0.842s=0.226,rmsep=0.362). Based on the results of this research, the most crucial descriptor that influence the free radical scavenge of the curcumins is the nsssN (count of atom-type E-state: >N-) descriptor with DC and MF values of 12.980 and 0.965 respectively.

Fullerene quinazolinone conjugates targeting Mycobacterium tuberculosis: a combined molecular docking, QSAR, and ONIOM approach

Fullerene and its derivatives may bind to biological molecules, causing inhibitory effects. In this context, investigations of interactions of fullerene-based conjugates with proteins are of general interest. Particularly, fullerene and its derivatives demonstrate antibacterial properties; and one of the potential targets for drug design and health therapy is the inhibition of 6-oxopurine phosphoribosyltransferase in Mycobacterium tuberculosis (PDB code: 4RHY). In this article, the binding interactions between a series of quinazoline-4(3H)-ones and their fullerene derivatives with the target transferase were computationally investigated. Initially, we developed predictive quantitative structure-activity relationships (QSAR) models. Next, we introduced a simplified calculation schema that allows to evaluate relative binding affinities and to reveal specific mechanisms of action. For this purpose, the molecular docking approach was utilized to identify the native poses of the 18 transferase inhibitors. The binding pocket of the target protein was isolated and semi-empirical, and hybrid ONIOM scoring functions at different levels of theory were used to treat the ligands and the isolated binding pocket. The agreement within the calculated binding-free energies trends, as well as the agreement with the experimental data, suggests that the developed calculation schema can be used to estimate relative binding affinities towards 4RH. The combination of quantum-chemical models and QSAR models could be applied for future design of new selective inhibitors.

Quantitative Structure Activity Relationship Analysis of Coumarins as Free Radical Scavengers by Genetic Function Algorithm

The antioxidant properties of coumarin derivatives using the 2,2ˈ -diphenyl-1- picrylhydrazyl (DPPH) radical scavenging assay were investigated by the application of Quantitative Structure Activity Relationship (QSAR) studies. The molecular structures were optimized and submitted for the generation of quantum chemical and molecular descriptors. Genetic Function Algorithm (GFA) was employed in model development. Also the applicability domain of the developed models were accessed by the leverage approach. The Variation Inflation Factor (VIF), Mean Effect (MF) and Degree of Contribution (DC) of these descriptors were estimated. Five predictive QSAR models were developed and subjected to various validation tests. The developed models gave highly encouraging results upon validation (R=0.938, R^2=0.879, Q^2=0.845, 〖R^2〗_pred=0.700 and cR_p^2= 0.832. This research indicates that the most crucial descriptors that influence the free radical scavenging activities and essential in the design of new set of the coumarin antioxidants are the HBDCount (hydrogen-bond donor count), AATS3e (Average Broto-Moreau autocorrelation - lag 3 / weighted by Sanderson electronegativities), and the MW (Molecular weight) descriptors.