Structure tuning of pyrazolylpyrrole derivatives as ERK inhibitors utilizing dual tools; 3D-QSAR and side-chain hopping

3D-QSAR, molecular docking, and molecular dynamics analysis of novel biphenyl-substituted pyridone derivatives as potent HIV-1 NNRTIs

When designing new medications targeting HIV-1, drug designers concentrate on reverse transcriptase (RT), the central enzyme of their concern. This is due to its vital role in converting single-stranded RNA into double-stranded DNA throughout the life cycle of HIV-1. In recent reports, a series of newly discovered pyridone derivatives with biphenyl substitutions have emerged as highly potent HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs), displaying impressive antiviral activity. To analyse the three-dimensional quantitative structure-activity relationship (3D-QSAR) of pyridone inhibitors with biphenyl substitutions, we employed CoMFA and CoMSIA methods in this study. The dataset comprises a total of 51 compounds. The findings of this research demonstrate that both the CoMFA (q2=0.688, r2=0.976, rpred2=0.831) and CoMSIA/SHE (q2=0.758, r2=0.968, rpred2=0.828) models exhibit excellent predictive capability and reliable estimation stability. According to the findings of the model, we designed a collection of eleven molecules that exhibit the potential for significantly improved predictive activity. We proceeded to investigate the binding patterns of these compounds to receptor proteins utilizing the molecular docking technique. To ensure the reliability of the docking results, we went on to validate them by conducting molecular dynamics simulations and performing accurate calculations of the binding free energy. Moreover, based on initial ADMET predictions, the results consistently indicate that the newly created molecule possesses favourable pharmacokinetic properties. This study will help to facilitate the development of efficient novel inhibitors that specifically target HIV-1's non-nucleoside reverse transcriptase (NNRTIs).Communicated by Ramaswamy H. Sarma.

Evaluation of novel HIV-1 protease inhibitors with DRV-resistance by utilizing 3D-QSAR molecular docking and molecular dynamics simulation

Molecular dynamics simulations were performed to explore the interaction mode of DRV derivatives binding to target proteins and to identify new potential HIV-1 PR inhibitors with stronger activity.

Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents

Two isoforms of extracellular regulated kinase (ERK), namely ERK-1 and ERK-2, are associated with several cellular processes, the aberration of which leads to cancer. The ERK-1/2 inhibitors are thus considered as potential agents for cancer therapy. Multitarget quantitative structure–activity relationship (mt-QSAR) models based on the Box–Jenkins approach were developed with a dataset containing 6400 ERK inhibitors assayed under different experimental conditions. The first mt-QSAR linear model was built with linear discriminant analysis (LDA) and provided information regarding the structural requirements for better activity. This linear model was also utilised for a fragment analysis to estimate the contributions of ring fragments towards ERK inhibition. Then, the random forest (RF) technique was employed to produce highly predictive non-linear mt-QSAR models, which were used for screening the Asinex kinase library and identify the most potential virtual hits. The fragment analysis results justified the selection of the hits retrieved through such virtual screening. The latter were subsequently subjected to molecular docking and molecular dynamics simulations to understand their possible interactions with ERK enzymes. The present work, which utilises in-silico techniques such as multitarget chemometric modelling, fragment analysis, virtual screening, molecular docking and dynamics, may provide important guidelines to facilitate the discovery of novel ERK inhibitors.

Discovery of CNS-Like D3R-Selective Antagonists Using 3D Pharmacophore Guided Virtual Screening

The dopamine D3 receptor is an important CNS target for the treatment of a variety of neurological diseases. Selective dopamine D3 receptor antagonists modulate the improvement of psychostimulant addiction and relapse. In this study, five and six featured pharmacophore models of D3R antagonists were generated and evaluated with the post-hoc score combining two survival scores of active and inactive. Among the Top 10 models, APRRR215 and AHPRRR104 were chosen based on the coefficient of determination (APRRR215: R²training = 0.80; AHPRRR104: R²training = 0.82) and predictability (APRRR215: Q²test = 0.73, R²predictive = 0.82; AHPRRR104: Q²test = 0.86, R²predictive = 0.74) of their 3D-quantitative structure⁻activity relationship models. Pharmacophore-based virtual screening of a large compound library from eMolecules (>3 million compounds) using two optimal models expedited the search process by a 100-fold speed increase compared to the docking-based screening (HTVS scoring function in Glide) and identified a series of hit compounds having promising novel scaffolds. After the screening, docking scores, as an adjuvant predictor, were added to two fitness scores (from the pharmacophore models) and predicted Ki (from PLSs of the QSAR models) to improve accuracy. Final selection of the most promising hit compounds were also evaluated for CNS-like properties as well as expected D3R antagonism.

The comparison of automated clustering algorithms for resampling representative conformer ensembles with RMSD matrix

Background

The accuracy of any 3D-QSAR, Pharmacophore and 3D-similarity based chemometric target fishing models are highly dependent on a reasonable sample of active conformations. Since a number of diverse conformational sampling algorithm exist, which exhaustively generate enough conformers, however model building methods relies on explicit number of common conformers.

Results

In this work, we have attempted to make clustering algorithms, which could find reasonable number of representative conformer ensembles automatically with asymmetric dissimilarity matrix generated from openeye tool kit. RMSD was the important descriptor (variable) of each column of the N × N matrix considered as N variables describing the relationship (network) between the conformer (in a row) and the other N conformers. This approach used to evaluate the performance of the well-known clustering algorithms by comparison in terms of generating representative conformer ensembles and test them over different matrix transformation functions considering the stability. In the network, the representative conformer group could be resampled for four kinds of algorithms with implicit parameters. The directed dissimilarity matrix becomes the only input to the clustering algorithms.

Conclusions

Dunn index, Davies–Bouldin index, Eta-squared values and omega-squared values were used to evaluate the clustering algorithms with respect to the compactness and the explanatory power. The evaluation includes the reduction (abstraction) rate of the data, correlation between the sizes of the population and the samples, the computational complexity and the memory usage as well. Every algorithm could find representative conformers automatically without any user intervention, and they reduced the data to 14–19% of the original values within 1.13 s per sample at the most. The clustering methods are simple and practical as they are fast and do not ask for any explicit parameters. RCDTC presented the maximum Dunn and omega-squared values of the four algorithms in addition to consistent reduction rate between the population size and the sample size. The performance of the clustering algorithms was consistent over different transformation functions. Moreover, the clustering method can also be applied to molecular dynamics sampling simulation results.

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-017-0208-0) contains supplementary material, which is available to authorized users.

In silico investigation of new binding pocket for mitogen activated kinase kinase (MEK): Development of new promising inhibitors

It has been previously shown that the inhibition of mitogen activated protein kinase kinase (MEK) contributes to apoptosis and suppression of different cancer cells. Correspondingly, a number of MEK1/2 inhibitors have been designed and evaluated since 2001. However, they did not satisfy essential pharmacokinetic (PK) and pharmacodynamic (PD) properties thus, almost most of them were terminated in pre-clinical or clinical studies. This study aims to design new specific MEK1/2 inhibitors with improved PK/PD profiles to be used as alternative cancer medications. In first part of this study, a comprehensive screening, for the first time, was done on well-known MEK1/2 inhibitors using a number of computational programs such as AutoDock Tools 4.2 (ADT) and AutoDock Vina. Therefore a valuable training dataset as well as a reliable pharmacophore model were provided which were then used to design new inhibitors. According to the results of training dataset, Trametinib was determined as the best inhibitor provided, so far. So, Trametinib was used as the lead structure to design new inhibitors in this study. In second part of this investigation, a set of new allosteric MEK1/2 inhibitors were designed significantly improving the binding energy as well as the ADMET properties, suggesting more specific and stable ligand-receptor complexes. Consequently, the structures 14 and 15 of our inhibitors, as the most potent structures, are great substituents for Trametinib to be used and evaluated in clinical trials as alternative cancer drugs.