Robust Quantitative Structure-activity Relationship Model Research Articles

Investigation of MI-2 analogues as MALT1 inhibitors to treat of diffuse large B-Cell lymphoma through combined molecular dynamics simulation, molecular docking and QSAR techniques and design of new inhibitors

Mucosa-associated lymphoid tissue lymphoma translocation protein-1 (MALT1) has emerged as a promising therapeutic target for the treatment of activated B cell–like diffuse large B-cell lymphoma (ABC-DLBCL). To develop more potent MALT1 inhibitors, a series of MI-2 analogues were investigated using combined molecular modeling techniques such as molecular dynamics (MD) simulation, molecular docking and quantitative structure-activity relationship (QSAR). 40 ns MD simulations were successfully performed on the docked complexes of reference compound (MI-2) and compound 33 (one of highly active compounds of the data set) to investigate the binding stability and specifying the active site of the MALT1 enzyme in interaction with inhibitors. A few key residues (GLY416 and GLU500) at the binding site of MALT1 enzyme were identified. Using molecular docking was understood that hydroxyl groups along with groups of dichlorophenyl, chlorobenzyl and fluorobenzyl help to create interactions between the MI-2 analogues and enzyme residues to stabilize the conformation of compounds at the binding pocket. A robust and reliable QSAR model, as evidenced by the high R2, Q2LOO and R2ext values (0.933, 0.891 and 0.970, respectively), was gained based on the MI-2 analogues to predict the quantitative chemical-biological relationship and predict the inhibitory activity of new drug compounds. Then ten new MI-2 analogues have been designed that provide better inhibitory activity with the MALT1 enzyme than the reference compounds.

Development and Validation of a Robust QSAR Model for Benzothiazole Hydrazone Derivatives as Bcl-XL Inhibitors

Background: B-cell Lymphoma Extra Large (Bcl-XL) belongs to B-cell Lymphoma two (Bcl-2) family. Due to its over-expression and anti-apoptotic role in many cancers, it has been proven to be a more biologically relevant therapeutic target in anti-cancer therapy. In this study, a Quantitative Structure Activity Relationship (QSAR) modeling was performed to establish the link between structural properties and inhibitory potency of benzothiazole hydrazone derivatives against Bcl-XL. Methods: The 53 benzothiazole hydrazone derivatives have been used for model development using genetic algorithm and multiple linear regression methods. The data set is divided into training and test set using Kennard-Stone based algorithm. The best QSAR model has been selected with statistically significant r2 = 0.931, F-test =55.488 RMSE = 0.441 and Q2 0.900. Results: The model has been tested successfully for external validation (r2 pred = 0.752), as well as different criteria for acceptable model predictability. Furthermore, analysis of the applicability domain has been carried out to evaluate the prediction reliability of external set molecules. The developed QSAR model has revealed that nThiazoles, nROH, EEig13d, WA, BEHv6, HATS6m, RDF035u and IC4 descriptors are important physico-chemical properties for determining the inhibitory activity of these molecules. Conclusion: The developed QSAR model is stable for this chemical series, indicating that test set molecules represent the training dataset. The model is statistically reliable with good predictability. The obtained descriptors reflect important structural features required for activity against Bcl-XL. These properties are designated by topology, shape, size, geometry, substitution information of the molecules (nThiazoles and nROH) and electronic properties. In a nutshell, these characteristics can be successfully utilized for designing and screening of novel inhibitors.

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.

Quantitative structure-activity relationship (QSAR) analysis of plant-derived compounds with larvicidal activity against Zika Aedes aegypti (Diptera: Culicidae) vector using freely available descriptors.

We have developed a quantitative structure-activity relationship (QSAR) model for predicting the larvicidal activity of 60 plant-derived molecules against Aedes aegypti L. (Diptera: Culicidae), a vector of several diseases such as dengue, yellow fever, chikungunya and Zika. The balanced subsets method (BSM) based on k-means cluster analysis (k-MCA) was employed to split the data set. The replacement method (RM) variable subset selection technique coupled with multivariable linear regression (MLR) proved to be successful for exploring 18 326 molecular descriptors and fingerprints calculated with PaDEL, Mold2 and EPI Suite open-source softwares. A robust QSAR model (Rtrain2=0.84, Strain = 0.20 and Rtest2=0.92, Stest = 0.23) involving five non-conformational descriptors was established. The model was validated and tested through the use of an external test set of compounds, the leave-one-out (LOO) and leave-more-out (LMO) cross-validation methods, Y-randomization and applicability domain (AD) analysis. The QSAR model surpasses previously published models based on geometrical descriptors, thereby representing a suitable tool for predicting larvicidal activity against the vector A. aegypti using a conformation-independent approach. © 2018 Society of Chemical Industry.

Quantum chemical descriptors in the QSAR studies of compounds active in maxima electroshock seizure test

DFT quantum mechanical method B3LYP/631G∗∗ was used to optimize the molecular geometry of some 2-amino-N-benzylacetamide derivatives with anticonvulsant activities. Molecular descriptors were extracted from the optimized structure and used together with their activity as the database for the study. Kennard-Stone algorithm, genetic function algorithm, and multiple linear regressions were used to build a robust quantitative structure-activity relationship model. The quality of the model was shown by its parameters: R2 (0.9270), R2adj (0.9178), F8,63(100.02), Q2 (0.9036) and R2pred (0.7406). Therefore, the model can be used to predict the activity of new chemicals that within its applicability domain. The x-component of molecular dipole moment (dx), HOMO-LUMO energy gap (Δ∊), electrophilicity index (Ω), square of ovality (Ф2), anisotropy of the polarizability (β2), topological electronic index (TE), square root of the sum of square of charges on all hydrogen (QH) and square root of the sum of square of charges on all nitrogen (QN) are the descriptors that influenced the anticonvulsant activity of the studied compounds. This information can be utilized in the future to optimize the anticonvulsant activity of the studied compounds.

Correlating methane production to microbiota in anaerobic digesters fed synthetic wastewater

A quantitative structure activity relationship (QSAR) between relative abundance values and digester methane production rate was developed. For this, 50 triplicate anaerobic digester sets (150 total digesters) were each seeded with different methanogenic biomass samples obtained from full-scale, engineered methanogenic systems. Although all digesters were operated identically for at least 5 solids retention times (SRTs), their quasi steady-state function varied significantly, with average daily methane production rates ranging from 0.09 ± 0.004 to 1 ± 0.05 L-CH4/LR-day (LR = Liter of reactor volume) (average ± standard deviation). Digester microbial community structure was analyzed using more than 4.1 million partial 16S rRNA gene sequences of Archaea and Bacteria. At the genus level, 1300 operational taxonomic units (OTUs) were observed across all digesters, whereas each digester contained 158 ± 27 OTUs. Digester function did not correlate with typical biomass descriptors such as volatile suspended solids (VSS) concentration, microbial richness, diversity or evenness indices. However, methane production rate did correlate notably with relative abundances of one Archaeal and nine Bacterial OTUs. These relative abundances were used as descriptors to develop a multiple linear regression (MLR) QSAR equation to predict methane production rates solely based on microbial community data. The model explained over 66% of the variance in the experimental data set based on 149 anaerobic digesters with a standard error of 0.12 L-CH4/LR-day. This study provides a framework to relate engineered process function and microbial community composition which can be further expanded to include different feed stocks and digester operating conditions in order to develop a more robust QSAR model.

High-dimensional QSAR modelling using penalized linear regression model with L1/2-norm

In high-dimensional quantitative structure–activity relationship (QSAR) modelling, penalization methods have been a popular choice to simultaneously address molecular descriptor selection and QSAR model estimation. In this study, a penalized linear regression model with L1/2-norm is proposed. Furthermore, the local linear approximation algorithm is utilized to avoid the non-convexity of the proposed method. The potential applicability of the proposed method is tested on several benchmark data sets. Compared with other commonly used penalized methods, the proposed method can not only obtain the best predictive ability, but also provide an easily interpretable QSAR model. In addition, it is noteworthy that the results obtained in terms of applicability domain and Y-randomization test provide an efficient and a robust QSAR model. It is evident from the results that the proposed method may possibly be a promising penalized method in the field of computational chemistry research, especially when the number of molecular descriptors exceeds the number of compounds.

Variable-selection approaches to generate QSAR models for a set of antichagasic semicarbazones and analogues

Quantitative structure–activity relationship (QSAR) models were proposed to correlate structural features or property descriptors of compounds with their corresponding biological activities. Because of the huge number of descriptors that encode different structural features used to generate valid QSAR models, variable selection becomes a fundamental step in building predictive and interpretative models. In this study, we applied a combined approach using multiple linear regression (MLR) and partial least-squares regression (PLS) to generate robust QSAR models with only a few descriptors applied to a set of cruzain inhibitors, namely, 61 semicarbazones and analogues, taken from the literature. From the 4885 descriptors generated by the Dragon program, we selected only five descriptors, applying the “Best-First” algorithm and PLS, followed by analysis of frequency and, finally, the genetic algorithm with MLR. The most significant QSAR equation encodes important steric and electronic structural features, which helps to identify in the set, structures that increase or decrease the pIC50 values measured against cruzain.

QSAR modeling of VOCs degradation by ferrous-activated persulfate oxidation

The objective of the study is to evaluate the degradability of volatile organic compounds (VOCs, in mixture) by sodium persulfate (SPS) with Fe(II) activation. Degradation of 51 VOCs by SPS alone and with 3 Fe(II)-based activators (i.e. ferrous ion, citric acid-chelated Fe(II), and EDTA-chelated Fe(II)) was investigated in batch experiments. In 48 h, 16 VOCs degraded over 90% with non-activated SPS, and 31 VOCs degraded over 90% with Fe(II) activation. The reaction rate constants of VOC degradation were also analyzed by quantitative structure-activity relationship (QSAR) model. Genetic algorithm and multiple linear regression analysis were applied to select the descriptors to build QSAR model. The main contribution to the degradation rate was given by energy level of highest occupied molecular orbital (EHOMO), double bond equivalent, and the largest negative partial net charge on a carbon atom (Qc-). Based on cluster analysis of degradation rates and main descriptors, the degradability of target VOCs were classified into three classes—rapid, moderate, and slow. The obtained statistically robust QSAR model can be used to estimate the removal efficiency of VOCs by persulfate radicals.

Molecular structural characteristics important in drug-HSA binding.

A non-linear quantitative structure activity relationship (QSAR) model based on 350 drug molecules was developed as a predictive tool for drug protein binding, by correlating experimentally measured protein binding values with ten calculated molecular descriptors using a radial basis function (RBF) neural network. The developed model has a statistically significant overall correlation value (r > 0.73), a high efficiency ratio (0.986), and a good predictive squared correlation coefficient (q(2)) of 0.532, which is regarded as producing a robust and high quality QSAR model. The developed model may be used for the screening of drug candidate molecules that have high protein binding data, filtering out compounds that are unlikely to be protein bound, and may assist in the dose adjustment for drugs that are highly protein bound. The advantage of using such a model is that the percentage of a potential drug candidate that is protein bound (PB (%)) can be simply predicted from its molecular structure.

QSAR models for anti-malarial activity of 4-aminoquinolines.

In the present study, predictive quantitative structure - activity relationship (QSAR) models for anti-malarial activity of 4-aminoquinolines have been developed. CORAL, which is freely available on internet (http://www.insilico.eu/coral), has been used as a tool of QSAR analysis to establish statistically robust QSAR model of anti-malarial activity of 4-aminoquinolines. Six random splits into the visible sub-system of the training and invisible subsystem of validation were examined. Statistical qualities for these splits vary, but in all these cases, statistical quality of prediction for anti-malarial activity was quite good. The optimal SMILES-based descriptor was used to derive the single descriptor based QSAR model for a data set of 112 aminoquinolones. All the splits had r(2)> 0.85 and r(2)> 0.78 for subtraining and validation sets, respectively. The three parametric multilinear regression (MLR) QSAR model has Q(2) = 0.83, R(2) = 0.84 and F = 190.39. The anti-malarial activity has strong correlation with presence/absence of nitrogen and oxygen at a topological distance of six.

Molecular docking and quantitative structure–activity relationship (QSAR) analyses of indolylarylsulfones as HIV-1 non-nucleoside reverse transcriptase inhibitors

Indolylarylsulfones (IASs) have received considerable interest during the last decades due to high potency against HIV-1 as non-nucleoside reverse transcriptase inhibitors. In present work, quantitative structure–activity relationship (QSAR) and molecular docking analyses were performed to model the anti-HIV-1 activity of 36 IASs. 2D and 3D-descriptors, genetic algorithm, internal and external validations were used to develop statistically robust four-parametric QSAR models. The best QSAR model is with R tr 2 = 0.8608. The QSAR analysis reveals that the activity of IASs depends on the presence of electronegative and heavy atoms at the internal atmosphere of the compounds. The docking analysis reveals that lipophilic and H-bonding interactions are the prominent interactions among IASs and the receptor. The QSAR analysis proved to be a useful tool in the prediction of anti-HIV-1 activity of congeneric compounds and some important insights were also found that will be useful to guide for the synthesis of new IASs with improved activity.

ANN-QSAR Model for Virtual Screening of Androstenedione C-Skeleton Containing Phytomolecules and Analogues for Cytotoxic Activity Against Human Breast Cancer Cell Line MCF-7

The present study deals with the development of an artificial neural network based quantitative structure activity relationship (QSAR) model for virtual screening of active compounds which contain androstenedione carbonskeleton or their similar skeleton at the core. An empirical data modeling (with fitted data mapping) has been performed on the basis of bioassay record for human breast cancer cell line MCF7. The whole experimental data set was considered as test set. Standard feed-forward back-propagation neural network technique was applied to build the model. Leave-One- Out (LOO) cross-validation was performed to evaluate the performance of the model. The mapped model became the basis for selection best mapped compounds followed by development of Pharmacophore specific secondary QSAR model. In the present study, two best mapped molecules '4beta-hydroxy Withanolide-E' and '7, 8-Dehydrocalotropin' were used for development of the secondary QSAR model. These secondary-QSAR models were resulted with R2 LOOCV value 0.9845 and 0.9666 respectively. Docking studies, in silico phamacokinetic and toxicity analysis was also done for selected compounds. The screened compounds CID_73621, CID_16757497, CID_301751, CID_390666 and CID_46830222 were found with promising binding affinity value with aromatase with reference to the co-crystallized control compound androstenedione. Due to excellent extent of variance coverage in ANN based QSAR map model, it can be used as a robust non-linear QSAR model for androstenedione carbon-skeleton containing molecules and the protocol can be used to derive secondary QSAR models for other compounds set.

Structural and chemical basis for enhanced affinity to a series of mycobacterial thymidine monophosphate kinase inhibitors: fragment-based QSAR and QM/MM docking studies

Tuberculosis (TB) still remains one of the most deadly infectious diseases. Mycobacterium tuberculosis thymidine monophosphate kinase (TMPKmt) has emerged as an attractive molecular target for the design of a novel class of anti-TB agents since blocking it will affect the pathways involved in DNA replication. Aiming at shedding some light on structural and chemical features that are important for the affinity of thymidine derivatives to TMPKmt, we have employed a special fragment-based method to develop robust quantitative structure-activity relationship models for a large and chemically diverse series of thymidine-based analogues. Significant statistical parameters (r2= 0.94, q2= 0.76, r2pred= 0.89) were obtained, indicating the reliability of the hologram QSAR model in predicting the biological activity of untested compounds. The 2D model was then used to predict the potency of an external test set, and the predicted values obtained from the HQSAR model were in good agreement with the experimental results. We have accordingly designed novel TMPKmt inhibitors by utilizing the fragments proposed by HQSAR analysis and predicted with good activity in the developed models. The new designed compounds also presented drug-like characteristics based on Lipinski's rule of 5. The generated molecular recognition patterns gathered from the HQSAR analysis combined with quantum mechanics/molecular mechanics (QM/MM) docking studies, provided important insights into the chemical and structural basis involved in the molecular recognition process of this series of thymidine analogues and should be useful for the design of new potent anti-TB agents.

Quantitative structure–activity relationship analysis of a novel series of chemicals antagonizing WT and MT AR

In the clinical treatment of prostate cancer, mutation in the androgen receptor (AR) that occurred in patients, often results in drug resistance because the agonist activity of the drug increases while the antagonist activity decreases. Recently a novel series of diazacycloundecane AR antagonists were reported, and their abilities to antagonize the wild type (WT) AR as well as antagonize the mutated type (MT) AR (T877A) were determined experimentally. In this paper, to understand more about the drug resistance and to help to design more active antagonists, the quantitative structure–activity relationship (QSAR) of these compounds were analyzed, and the different interactions of these antagonists with two types of AR were compared. As a result two robust and predictive QSAR models were proposed, and the different structure features related with the antagonist activities on WT and MT AR were analyzed separately.

Quantum Mechanical Methods for Drug Design

Quantum mechanical (QM) methods are becoming popular in computational drug design and development mainly because high accuracy is required to estimate (relative) binding affinities. For low-to medium-throughput in silico screening, (e.g., scoring and prioritizing a series of inhibitors sharing the same molecular scaffold) efficient approximations have been developed in the past decade, like linear scaling QM in which the computation time scales almost linearly with the number of basis functions. Furthermore, QM-based procedures have been used recently for determining protonation states of ionizable groups, evaluating energies, and optimizing molecular structures. For high-throughput in silico screening QM approaches have been employed to derive robust quantitative structure-activity relationship models. It is expected that the use of QM methods will keep growing in all phases of computer-aided drug design and development. However, extensive sampling of conformational space and treatment of solution of macromolecules are still limiting factors for the broad application of QM in drug design.

Application of quantitative structure–activity relationships for modeling drug and chemical transport across the human placenta barrier: a multivariate data analysis approach

Pharmacological agents and environmental pollutants can transfer from mother to fetus across the placental barrier, leading to reproductive toxic effects. Ex vivo human placental perfusion constitutes the most widely used method to study placental transfer and metabolism of drugs and chemicals. The aim of the present study was to evaluate whether quantitative structure-activity relationship (QSAR) methodology could serve as an effective alternative tool to estimate drugs and chemicals transport across the human placental barrier on the basis of easily interpretable molecular, physicochemical and structural properties. Multivariate data analysis (MVDA) was applied to a set of 88 structurally diverse drugs and chemicals to model placental transfer expressed by clearance index values compiled from literature sources. An adequate and robust QSAR model (r(2) = 0.73, Q(2) = 0.71, RMSEE = 0.15) was established, providing an informative illustration of the contributing physicochemical, molecular and structural properties of the compounds in placental transfer process. Descriptors reflecting the polarity of compounds proved to be the most important with a negative sign. Lipophilicity and, at a lower extent, molecular size parameters exerted positive contribution in the model. Thus, QSAR analysis may be considered as a promising alternative tool to support high-throughput screening of drugs and chemicals in respect to their transport across placental barrier.

Quantitative structure–activity relationship (QSAR) methodology in forensic toxicology: Modeling postmortem redistribution of structurally diverse drugs using multivariate statistics

Postmortem redistribution (PMR) constitutes a multifaceted process, which renders the analytical results of drug concentrations inaccurate to be interpreted by forensic toxicologists. The aim of the present study was to evaluate whether quantitative structure–activity relationship (QSAR) methodology could serve as an effective tool to estimate the ability of drugs to redistribute across tissue barriers during postmortem period on the basis of their molecular, physicochemical and structural properties. In this aspect, multivariate data analysis (MVDA) was applied to a set of 77 structurally diverse drugs. PMR data expressed by the central:peripheral concentration ratio (C:P ratio) was taken from the literature. An adequate and robust QSAR model ( R 2 = 0.65, Q 2 = 0.56, RMSEE = 0.34) was established for 59 (77%) out of 77 drugs. Although the derived QSAR model presented limited applicability, it provided an informative illustration of the contributing molecular, physicochemical and structural properties in PMR process. Drugs with strong basic properties and enhanced molecular size, flexibility, lipophilicity and number of halogens were found to be susceptible to increased PMR. Due to the high complexity of PMR process, further QSAR studies need to focus on structurally related drugs to develop more specific models, which could serve as alternative tools to evaluate PMR for different chemical classes.

Fragment‐Based QSAR and Molecular Modeling Studies on a Series of Discodermolide Analogs as Microtubule‐Stabilizing Anticancer Agents

AbstractInhibition of microtubule function is an attractive rational approach to anticancer therapy. Although taxanes are the most prominent among the microtubule‐stabilizers, their clinical toxicity, poor pharmacokinetic properties, and resistance have stimulated the search for new antitumor agents having the same mechanism of action. Discodermolide is an example of nontaxane natural product that has the same mechanism of action, demonstrating superior antitumor efficacy and therapeutic index. The extraordinary chemical and biological properties have qualified discodermolide as a lead structure for the design of novel anticancer agents with optimized therapeutic properties. In the present work, we have employed a specialized fragment‐based method to develop robust quantitative structure–activity relationship models for a series of synthetic discodermolide analogs. The generated molecular recognition patterns were combined with three‐dimensional molecular modeling studies as a fundamental step on the path to understanding the molecular basis of drug–receptor interactions within this important series of potent antitumoral agents.

Application of ‘HESH’ Descriptors for the Structure-Activity Relationships of Antimicrobial Peptides

In this paper, HESH, which was a new set of amino acid descriptors including Hydrophobic, Electronic, Steric and Hydrogen bond contribution properties, were derived from multi-dimensional properties of 20 coded amino acids. The quantitative structure-activity relationship (QSAR) of 101 synthetic cationic antimicrobial polypeptides (CAMELs) was then characterized with HESH scales and studied by genetic algorithm-partial least square (GA-PLS) method. It was found that the robust QSAR model constructed with electronic and hydrophobic properties parameters of HESH descriptors was a better one. Through further analysis, electronic and hydrophobic properties of the 3rd, 6th, 7th, 11th and 12th residue of CAMELs sequence made high contribution to antimicrobial potencies. Based on this PLS model, a series of cationic antimicrobial peptides (AMPs), with relatively high antimicrobial activity was designed. Meanwhile, a robust QSAR model with favorable predictive capability for 34 antimicrobial peptides was constructed with HESH descriptors. The results showed that HESH descriptors had many obvious advantages, for it contains abundant information and its physico-chemical characteristics are clear and easily explained. The developed descriptors can be further expanded for the larger sets of biologically activities peptides and can serve as a useful quantitative tool for the rational design and discovery of antibiotics.