Molecular dynamics-guided receptor-dependent 4D-QSAR studies of HDACs inhibitors.

Histone deacetylases (HDACs) were highlighted as a novel category of anticancer targets. Several HDACs inhibitors were approved for therapeutic use in cancer treatment. Comparatively, receptor-dependent 4D-QSAR, LQTA-QSAR, is a new approach which generates conformational ensemble profiles of compounds by molecular dynamics simulations at binding site of enzyme. This work describes a receptor-dependent 4D-QSAR studies on hydroxamate-based HDACs inhibitors. The 4D-QSAR model was generated by multiple linear regression method of QSARINS. Leave-N-out cross-validation (LNO) and Y-randomization were performed to analysis of the independent test set and to verify the robustness of the model. Best 4D-QSAR model showed the following statistics: R2 = 0.8117, Q2LOO = 0.6881, Q2LNO = 0.6830, R2Pred = 0.884. The results may be used for further virtual screening and design for novel HDACs inhibitors. The receptor dependent 4D-QSAR model was developed for the hydroxamate derivatives as HDAC inhibitors by making use of molecular dynamics simulation to obtain conformational ensemble profile for each compound. The multiple linear regression method was used to generate 4D-QSAR model with the suitable predictive ability and the excellent statistical parameters.

4D-QSAR Molecular Modeling and Analysis of Flavonoid Derivatives as Acetylcholinesterase Inhibitors.

Flavonoids are potential strikingly natural compounds with antioxidant activity and acetylcholinesterase (AChE) inhibitory activity for treating Alzheimer's disease (AD). In present study, in line with our interests in flavonoid derivatives as AChE inhibitors, a four-dimensional quantitative structure-activity relationship (4D-QSAR) molecular model was proposed. The data required to perform 4D-QSAR analysis includes 52 compounds reported in the literature, usually analogs, and their measured biological activities in a common assay. The model was generated by a complete set of 4D-QSAR program which was written by our group. The best model was found after trying multiple experiments. It had a good predictive ability with the cross-validation correlation coefficient Q2 = 0.77, the internal validation correlation coefficient R2 = 0.954, and the external validation correlation coefficient R2pred = 0.715. The molecular docking analysis was also carried out to understand exceedingly the interactions between flavonoids and the AChE targets, which was in good agreement with the 4D-QSAR model. Based on the information provided by the 4D-QSAR model and molecular docking analysis, the idea for optimizing the structures of flavonoids as AChE inhibitors was put forward which maybe provide theoretical guidance for the research and development of new AChE inhibitors.

Leishmanicidal and cytotoxic activities and 4D-QSAR of 2-arylidene indan-1,3-diones.

The indan-1,3-dione and its derivatives are important building blocks in organic synthesis and present important biological activities. Herein, the leishmanicidal and cytotoxicity evaluation of 16 2-arylidene indan-1,3-diones is described. The compounds were evaluated against the leukemia cell lines HL60 and Nalm6, and the most effective ones were 2-(4-nitrobenzylidene)-1H-indene-1,3(2H)-dione (4) and 4-[(1,3-dioxo-1H-inden-2(3H)-ylidene)methyl]benzonitrile (10), presenting IC50 values of around 30 µmol/L against Nalm6. The leishmanicidal activity was assessed on Leishmania amazonensis, with derivative 4 (IC50 = 16.6 µmol/L) being the most active. A four-dimensional quantitative structure-activity analysis (4D-QSAR) was applied to the indandione derivatives, through partial least-squares regression. The statistics presented by the regression models built with the selected field descriptors of Coulomb (C) and Lennard-Jones (L) nature, considering the activities against L. amazonensis, HL60, and Nalm6 leukemia cells, were, respectively, R2 = 0.88, 0.92, and 0.98; Q2 = 0.83, 0.88, and 0.97. The presence of positive Coulomb descriptors near the carbonyl groups indicates that these polar groups are related to the activities. Besides, the presence of positive Lennard-Jones descriptors close to substituents R3 or R1 indicates that bulky nonpolar substituents in these positions tend to increase the activities. This study provides useful insights into the mode of action of indandione derivatives for each biological activity involved.

Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated 'bioactive' 3D ligand conformation is constructed as a 'sophisticated guess' (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis-sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its 'dialects' have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the 'mainstream' algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.

Web-4D-QSAR: A web-based application to generate 4D-QSAR descriptors.

A web-based application is developed to generate 4D-QSAR descriptors using the LQTA-QSAR methodology, based on molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. The LQTAGrid module calculates the intermolecular interaction energies at each grid point, considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. A friendly front end web interface, built using the Django framework and Python programming language, integrates all steps of the LQTA-QSAR methodology in a way that is transparent to the user, and in the backend, GROMACS and LQTAGrid are executed to generate 4D-QSAR descriptors to be used later in the process of QSAR model building. © 2018 Wiley Periodicals, Inc.

Structural insights of dipeptidyl peptidase-IV inhibitors through molecular dynamics-guided receptor-dependent 4D-QSAR studies.

Dipeptidyl peptidase-IV (DPP-IV) inhibitors are promising antidiabetic agents. Currently, several DPP-IV inhibitors have been approved for therapeutic use in diabetes mellitus. Receptor-dependent 4D-QSAR is comparatively a new approach which uses molecular dynamics simulations to generate conformational ensemble profiles of compounds representing a dynamic state of compounds at a target's binding site. This work describes a receptor-dependent 4D-QSAR study on triazolopiperazine derivatives. QSARINS multiple linear regression method was adopted to generate 4D-QSAR models. A model with 9 variables was found to have better predictive accuracy with [Formula: see text], [Formula: see text] (leave-one-out) = 0.592 and [Formula: see text] predicted = 0.597. The location of these 9 variables at the binding site of DPP-IV revealed the importance of the residues Val711, Tyr662, Tyr666, Val202, Asp200 and Thr199 in making critical interactions with DPP-IV inhibitors. The study of these critical interactions revealed the structural features required in DPP-IV inhibitors. Thus, in this study the importance of a halogen substituent on a phenyl ring, the extent of substitution on the triazolopiperazine ring, the presence of an ionizable amino group and the presence of a hydrophobic substituent that can bind deeper in binding pocket of DPP-IV were revealed.

Benzo[e]pyrimido[5,4-b][1,4]diazepin-6(11H)-one derivatives as Aurora A kinase inhibitors: LQTA-QSAR analysis and detailed systematic validation of the developed model.

Aurora kinases are sub-divided into Aurora A, Aurora B, and Aurora C kinases that are considered as prospective targets for a new class of anticancer drugs. In this work, a 4-D-QSAR model using an LQTA-QSAR approach with previously reported 31 derivatives of benzo[e]pyrimido[5,4 -b][1,4]diazepin -6(11H)-one as potent Aurora kinase A inhibitors has been created. Instead of single conformation, the conformational ensemble profile generated for each ligand by using trajectories and topology information retrieved from molecular dynamics simulations from GROMACS package were aligned and used for the calculation of intermolecular interaction energies at each grid point. The descriptors generated on the basis of these Coulomb and Lennard-Jones potentials as independent variables were used to perform a PLS analysis using biological activity as dependent variable. A good predictive model was generated with nine field descriptors and five latent variables. The model showed [Formula: see text]; [Formula: see text] and [Formula: see text]. This model was further validated systematically by using different validation parameters. This 4D-QSAR model gave valuable information to recognize features essential to adapt and develop novel potential Aurora kinase inhibitors.

4D-QSAR and MIA-QSAR Studies of Aminobenzimidazole Derivatives as Fourth-generation EGFR Inhibitors.

BACKGROUND: The epidermal growth factor receptor (EGFR) protein has been intensively studied as a therapeutic target for non-small cell lung cancer (NSCLC). The aminobenzimidazole derivatives as the fourth-generation EGFR inhibitors have achieved promising results and overcame EGFR mutations at C797S, del19 and T790M in NSCLC. OBJECTIVE: In order to understand the quantitative structure-activity relationship (QSAR) of aminobenzimidazole derivatives as EGFRdel19 T790M C797S inhibitors, the four-dimensional QSAR (4D-QSAR) and multivariate image analysis (MIA-QSAR) have been performed on the data of 45 known aminobenzimidazole derivatives. METHODS: The 4D-QSAR descriptors were acquired by calculating the association energies between probes and aligned conformational ensemble profiles (CEP), and the regression models were established by partial least squares (PLS). In order to further understand and verify the 4D-QSAR model, MIA-QSAR was constructed by using chemical structure pictures to generate descriptors and PLS regression. Furthermore, the molecular docking and averaged noncovalent interactions (aNCI) analysis were also performed to further understand the interactions between ligands and the EGFR targets, which was in good agreement with the 4D-QSAR model. RESULTS: The established 4D-QSAR and MIA-QSAR models have strong stability and good external prediction ability. CONCLUSION: These results will provide theoretical guidance for the research and development of aminobenzimidazole derivatives as new EGFRdel19 T790M C797S inhibitors.

4D-QSAR, ADMET properties, and molecular dynamics simulations for designing N-substituted urea/thioureas as human glutaminyl cyclase inhibitors.

Human glutaminyl cyclase (hQC) inhibitors have great potential to be used as anti- Alzheimer's disease (AD) agents by reducing the toxic pyroform of ß-amyloid in the brains of AD patients. The four-dimensional quantitative structure activity relationship (4D-QSAR) model of N-substituted urea/thioureas was established with satisfying predictive ability and statistical reliability (Q2 = 0.521, R2 = 0.933, R2prep = 0.619). By utilizing the developed 4D-QSAR model, a set of new N-substituted urea/thioureas was designed and evaluated for their Absorption Distribution Metabolism Excretion and Toxicity (ADMET) properties. The results of molecular dynamics (MD) simulations, Principal component analysis (PCA), free energy landscape (FEL), dynamic cross-correlation matrix (DCCM) and molecular mechanics generalized Born Poisson-Boltzmann surface area (MM-PBSA) free energy calculations, revealed that the designed compounds were remained stable in protein binding pocket and compounds b ∼ f (-35.1 to -44.55 kcal/mol) showed higher binding free energy than that of compound 14 (-33.51 kcal/mol). The findings of this work will be a theoretical foundation for further research and experimental validation of urea/thiourea derivatives as hQC inhibitors.

Leishmanicidal activity and 4D quantitative structure-activity relationship and molecular docking studies of vanillin-containing 1,2,3-triazole derivatives.

Aim: The assessment of the antileishmanial potential of 22 vanillin-containing 1,2,3-triazole derivatives against Leishmania braziliensis is reported. Materials & methods: Initial screening was performed against the parasite promastigote form. The most active compound, 4b, targeted parasites within amastigotes (IC50 = 4.2 ± 1.0 µmol l-1), presenting low cytotoxicity and a selective index value of 39. 4D quantitative structure-activity relationship and molecular docking studies provided insights into structure-activity and biological effects. Conclusion: A vanillin derivative with significant antileishmanial activity was identified. Enhanced activity was linked to increased electrostatic and Van der Waals interactions near the benzyl ring of the derivatives. Molecular docking indicated the inhibition of the Leishmania amazonensis sterol 14α-demethylase, using Leishmania infantum sterol 14α-demethylase as a model, without affecting the human isoform. Inhibition was active site competition with lanosterol.

Synthesis, antiproliferative and 4D-QSAR studies of thiadiazole derivatives bearing acrylamide moiety as EGFR inhibitors.

As a target for clinical anti-cancer treatment, epidermal growth factor receptor (EGFR) exhibits its over-expression on various tumour cells and is associated with the development of a variety of human cancers. Herein, we described the synthesis, antiproliferative activity assay and 4D-QSAR studies of thiadiazole derivatives bearing acrylamide moiety as EGFR inhibitors. Compared with Gefitinib, some of the target compounds have excellent antiproliferative activities against EGFR-expressed A431 cell line. The robust and reliable 4D-QSAR was constructed using comparative distribution detection algorithm, ordered predictors selection and genetic algorithm method, and the following acceptable statistics are shown: r2 = 0.82, Q2LOO = 0.67, Q2LMO = 0.61, r2Pred = 0.78.

Quantitative structure-activity relationship and machine learning studies of 2-thiazolylhydrazone derivatives with anti-Cryptococcus neoformans activity.

Cryptococcus neoformans is a fungus responsible for infections in humans with a significant number of cases in immunosuppressed patients, mainly in underdeveloped countries. In this context, the thiazolylhydrazones are a promising class of compounds with activity against C. neoformans. The understanding of the structure-activity relationship of these derivatives could lead to the design of robust compounds that could be promising drug candidates for fungal infections. Specifically, modern techniques such as 4D-QSAR and machine learning methods were employed in this work to generate two QSAR models (one 2D and one 4D) with high predictive power (r2 for the test set equals to 0.934 and 0.831, respectively), and one random forest classification model was reported with Matthews correlation coefficient equals to 1 and 0.62 for internal and external validations, respectively. The physicochemical interpretation of selected models, indicated the importance of aliphatic substituents at the hydrazone moiety to antifungal activity, corroborating experimental data.Communicated by Ramaswamy H. Sarma.

Combined 4D-QSAR and target-based approaches for the determination of bioactive Isatin derivatives.

The hybrid method of the Electron-Conformational Genetic Algorithm (EC-GA) was used to determine the pharmacophore groups and to estimate anticancer activity in isatin derivatives using a robust 4D-QSAR software (EMRE). To build the model, each compound is represented by a set of conformers rather than a single conformation. The Electron Conformational Matrix of Congruity (ECMC) is composed via EMRE software. Electron Conformational Submatrix of Activity (ECSA) was calculated by the comparison of these matrices. Genetic algorithm was used to select important variables to predict theoretical activity. The model with the best seven parameters produced satisfactory results. The E statistics technique was applied to the generated EC-GA model to evaluate the individual contribution of each of the descriptors on biological activity. The r2 and q2 values of the training set compounds were found to be 0.95 and 0.93, respectively. Because no previous 4D-QSAR studies on isatin derivatives have been conducted, this study is important in the development of new isatin derivatives. In this study, 27 isatin derivatives whose activities were estimated using the hybrid EC-GA method were also investigated through molecular docking and molecular dynamics simulations for their BCL-2 inhibitory activity.

Synthesis and 4D-QSAR Studies of Alanine Hydroxamic Acid Derivatives as Aminopeptidase N Inhibitors.

BACKGROUND: As a target for anticancer treatment, aminopeptidase N (APN) shows its overexpression on diverse malignant tumor cells and associates with cancer invasion, angiogenesis and metastasis. OBJECTIVE: The objective of the study was the design, synthesis and biological activity evaluation of alanine hydroxamic acid derivatives as APN inhibitors, and investigation of the binding mode of inhibitors in the APN active site. METHODS: Alanine hydroxamic acid derivatives were synthesized and evaluated for their in vitro anti-cancer activity using CCK-8 assay. Molecular docking and 4D-QSAR studies were carried out to suggest the mechanism of biological activity. RESULTS: Compared with Bestatin, compound 9b showed the best APN inhibition activity. The putative binding mode of 9b in the APN active site was also discussed. Moreover, the robust and reliable 4D-QSAR model exhibited the following statistics: R2 = 0.9352, q2 LOO = 0.8484, q2 LNO =0.7920, R2 Pred = 0.8739. CONCLUSION: Newly synthesized compounds exerted acceptable anticancer activity and further investigation of the current scaffold would be beneficial.

4D-QSAR Models Applied to the Study of TGF- ß1 Receptor Inhibitors.

BACKGROUND: The quantitative structure-activity relationship is an analysis method that can be applied for designing new molecules. In 1997, Hopfinger and coworkers developed the 4DQSAR methodology aiming to eliminate the question of which conformation to use in a QSAR study. In this work, the 4D-QSAR methodology was used to quantitatively determine the influence of structural descriptors on the activity of aryl pyrimidine derivatives as inhibitors of the TGF-ß1 receptor. The members of the TGF-ß subfamily are interesting molecular targets, since they play an important function in the growth and development of cell cellular including proliferation, apoptosis, differentiation, Epithelial-Mesenchymal Transition (EMT), and migration. In late stages, TGF-ß exerts tumor-promoting effects, increasing tumor invasiveness, and metastasis. Therefore, TGF-ß is an attractive target for cancer therapy. OBJECTIVE: The major goal of the current research is to develop 4D-QSAR models aiming to propose new structures of aryl pyrimidine derivatives. MATERIALS AND METHODS: Molecular dynamics simulation was carried out to generate the conformational ensemble profile of a data set with aryl pyrimidine derivatives. The conformations were overlaid into a three-dimensional cubic box, according to the three-ordered atom alignment. The occupation of the grid cells by the interaction of pharmacophore elements provides the Grid Cell Occupancy Descriptors (GCOD), the dependent variables used to build the 4D-QSAR models. The best models were validated (internal and external validation) using several statistical parameters. Docking molecular studies were performed to better understand the binding mode of pyrimidine derivatives inside the TGF-ß active site. RESULTS: The 4D-QSAR model presented seven descriptors and acceptable statistical parameters (R2 = 0.89, q2 = 0.68, R2 pred = 0.65, r2 m = 0.55, R2 P = 0.68 and R2 rand = 0.21) besides pharmacophores groups important for the activity of these compounds. The molecular docking studies helped to understand the pharmacophoric groups and proposed substituents that increase the potency of aryl pyrimidine derivatives. CONCLUSION: The best QSAR model showed adequate statistical parameters that ensure their fitness, robustness, and predictivity. Structural modifications were assessed, and five new structures were proposed as candidates for a drug for cancer treatment.

Synthesis and receptor dependent 4D-QSAR studies of 4,5-dihydro-1,3,4-oxadiazole derivatives targeting cannabinoid receptor.

Cannabinoid receptor has been shown to be overexpressed in various types of cancers, especially non-small cell lung cancer. As a result, it could be used as novel target for anticancer treatments. Because receptor-dependent 4D-QSAR generates conformational ensemble profiles of compounds by molecular dynamics simulations at the binding site of the enzyme, this work describes the synthesis, biological activity evaluation and 4D-QSAR studies of 4,5-dihydro-1,3,4-oxadiazole derivatives targeting cannabinoid receptor. Compared with WIN55,212-2, compound 5 f showed the best antiproliferative activity. The receptor-dependent 4D-QSAR model was generated by multiple linear regression method using QSARINS. Leave-n-out cross-validation and chemical applicability domain were performed to analyse the independent test set and to verify the robustness of the model. The best 4D-QSAR model showed the following statistics: r2 = 0.8487, Q2LOO = 0.7667, Q2LNO = 0.7524, and r2Pred = 0.8358.

Synthesis, biological activity, and four-dimensional quantitative structure-activity analysis of 2-arylidene indan-1,3-dione derivatives tested against Daphnia magna.

A series of 18 2-arylidene indan-1,3-dione derivatives was synthesized and tested against Daphnia magna to assess the environmental toxicity of these compounds. Aiming to investigate the toxicity mechanism for this series of compounds, a four-dimensional quantitative structure-activity analysis (4D-QSAR) was performed through the partial least square regression (PLS). The best PLS model was built with two factors and the selected field descriptors, of Coulomb (C) and Lennard-Jones (L) nature, describing 77.43% of variance and presenting the following statistics: r 2 = 0.89; SEC = 0.30; Q 2 = 0.81; SEV = 0.36. According to the literature, the bioactivity of α,ß-unsaturated ketones, a functionality present in the series of compounds under investigation, is related to the conjugated double bond with the carbonyl group. The presence of a positive Coulomb descriptor nearby the carbonyl moieties, obtained as a result of the regression model, indicates that these polar groups are also related to the toxicity on D. magna. From the PLS regression model, the toxicity EC50-48 h values increases with the positive Coulomb descriptor and diminishes with the negative Lennard-Jones descriptors. It could be concluded that the presence of small polar groups in the aromatic ring of the arylidene moiety tends to increase the toxicity, while bulkier apolar substituents lead to a decrease of the toxicity.

Multiple Conformer Descriptors for QSAR Modeling.

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

The effect of stereoisomerism on the 4D-QSAR study of some dipeptidyl boron derivatives.

The electron conformational genetic algorithm (EC-GA) method had been employed by distinguishing between enantiomers for the first time as a 4D-QSAR approach to reveal the pharmacophore (Pha) and to predict the bioactivity of the dipeptidyl boron compounds. The Electron Conformational Matrices of Congruity (ECMCs) were prepared for all conformers of compounds in the data set based on the quantum chemical calculations at HF/3-21 G level in an aqueous medium. The comparison of the ECMCs within the certain tolerances by the EMRE program revealed the pharmacophore for some dipeptidyl boron derivatives. For the selection of the most influential parameters on the activity and the calculation of theoretical activities, the genetic algorithm with the non-linear least square method was used. The final model was validated by the cross-validation method with the division of the data set into training and test items. The 12-parameter model gave excellent statistical results (R2training = 0.850, R2test = 0.809, q2 = 0.755, q2ext1 = 0.776, q2ext2 = 0.759, q2ext3 = 0.735, CCCtr = 0.922, CCCtest = 0.846, CCCall = 0.905). Because of the inexistence of 4D-QSAR studies on the dipeptidyl boron derivatives and the stereoisomerism effect on the biological activity was examined for the first time for these compounds, this study plays an important role in the development of new boron-containing compounds.

4D-QSAR and MIA-QSAR study on the Bruton's tyrosine kinase (Btk) inhibitors.

In this paper, 4D-QSAR analysis base on LQTA-QSAR method and MIA-QSAR analysis were presented. And a combination model of 4D-QSAR model and MIA-QSAR model with better predictive performance was built. The 4D-QSAR descriptors were obtained by calculating the interaction energies between the probes and aligned conformational ensemble profiles (CEP) resulting from molecular dynamics simulation. The MIA descriptors were generated from aligned images of chemical structure. Those descriptors were filtered and employed in partial least squares (PLS) regression. The combination model was built by the merging the descriptors generated by the two methods of 4D-QSAR and MIA-QSAR analysis. Those models were built using programs written by authors, and anyone can download those programs at https://github.com/masgils.