3D-QSAR and docking studies of HIV-1 protease inhibitors using R-group search and Surflex-dock

QSAR Study, Molecular Docking and Molecular Dynamic Simulation of Aurora Kinase Inhibitors Derived from Imidazo[4,5-b]pyridine Derivatives

Cancer is a serious threat to human life and social development and the use of scientific methods for cancer prevention and control is necessary. In this study, HQSAR, CoMFA, CoMSIA and TopomerCoMFA methods are used to establish models of 65 imidazo[4,5-b]pyridine derivatives to explore the quantitative structure-activity relationship between their anticancer activities and molecular conformations. The results show that the cross-validation coefficients q2 of HQSAR, CoMFA, CoMSIA and TopomerCoMFA are 0.892, 0.866, 0.877 and 0.905, respectively. The non-cross-validation coefficients r2 are 0.948, 0.983, 0.995 and 0.971, respectively. The externally validated complex correlation coefficients r2pred of external validation are 0.814, 0.829, 0.758 and 0.855, respectively. The PLS analysis verifies that the QSAR models have the highest prediction ability and stability. Based on these statistics, virtual screening based on R group is performed using the ZINC database by the Topomer search technology. Finally, 10 new compounds with higher activity are designed with the screened new fragments. In order to explore the binding modes and targets between ligands and protein receptors, these newly designed compounds are conjugated with macromolecular protein (PDB ID: 1MQ4) by molecular docking technology. Furthermore, to study the nature of the newly designed compound in dynamic states and the stability of the protein-ligand complex, molecular dynamics simulation is carried out for N3, N4, N5 and N7 docked with 1MQ4 protease structure for 50 ns. A free energy landscape is computed to search for the most stable conformation. These results prove the efficient and stability of the newly designed compounds. Finally, ADMET is used to predict the pharmacology and toxicity of the 10 designed drug molecules.

QSAR analysis of 3-pyrimidin-4-yl-oxazolidin-2-one derivatives isocitrate dehydrogenase inhibitors using Topomer CoMFA and HQSAR methods

A series of mIDH1 inhibitors derived from 3-pyrimidine-4-oxazolidin-2-ketone derivatives were studied by QSAR model to explore the key factors that inhibit mIDH1 activity. The generated model was cross-verified and non-cross-verified by Topomer CoMFA and HQSAR methods; the independent test set was verified by PLS method; the Topomer search technology was used for virtual screening and molecular design; and the Surflex-Dock method and ADMET technology were used for molecular docking, pharmacology and toxicity prediction of the designed drug molecules. The Topomer CoMFA and HQSAR cross-validation coefficients q² are 0.783 and 0.784, respectively, and the non-cross-validation coefficients r² are 0.978 and 0.934, respectively. Ten new drug molecules have been designed using Topomer search technology. The results of molecular docking and ADMET show that the newly designed drug molecules are effective. The docking situation, pharmacology and toxicity prediction results are good. The model can be used to predict the bioactivity of the same type of new compounds and their derivatives. The prediction results of molecular design, molecular docking and ADMET can provide some ideas for the design and development of novel mIDH1 inhibitor anticancer drugs, and provide certain theoretical basis of the experimental verification of new compounds in the future. Newly designed molecules after docking with corresponding proteins in the PDB library, it can explore the targets of drug molecules acting with large proteins and the related force, which is very helpful for the design of new drugs and the mechanism of drug action.

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

BACKGROUND

Inhibition of HIV-I protease enzyme is a strategic step for providing better treatment in retrovirus infections, which avoids resistance and possesses less toxicity.

OBJECTIVES

In the course of our research to discover new and potent protease inhibitors, 3D-QSAR (CoMFA and CoMSIA) models were generated using 3 different alignment techniques, including multifit alignment, docking based and Distill based alignment for 63 compounds. Novel molecules were designed from the output of this study.

METHODS

A total of 3 alignment methods were used to generate CoMFA and CoMSIA models. A Distill based alignment method was considered a better method according to different validation parameters. A 3D-QSAR model was generated and contour maps were discussed. The biological activity of designed molecules was predicted using the generated QSAR model to validate QSAR. The newly designed molecules were docked to predict binding affinity.

RESULTS

In CoMFA, leave one out cross-validated coefficient (q²), conventional coefficient (r²) and predicted correlation coefficient (r²_Predicted) values were found to be 0.721, 0.991 and 0.780, respectively. The best obtained CoMSIA model also showed significant cross-validated coefficient (q²), conventional coefficient (r²) and predicted correlation coefficient (r²_Predicted) values of 0.714, 0.987 and 0.721, respectively. Steric and electrostatic contour maps generated from CoMFA and hydrophobic and hydrogen bond donor and hydrogen bond acceptor contour maps from CoMSIA models were used to design new and bioactive protease inhibitors by incorporating bioisosterism and knowledge-based structure-activity relationship.

CONCLUSION

The results from both these approaches, ligand-based drug design and structure-based drug design, are adequate and promising to discover protease inhibitors.

Structural modification of 4, 5-dihydro-[1, 2, 4] triazolo [4, 3-f] pteridine derivatives as BRD4 inhibitors using 2D/3D-QSAR and molecular docking analysis

Cancer treatment continues to be one of the most serious public health issues in the world. The overexpression of BRD4 protein has led to a series of malignant tumors, hence the development of small molecule BRD4 protease inhibitors has always been a hot spot in the field of medical research. In this study, a series of 4,5-dihydro-[1, 2, 4] triazolo [4, 3-f] pteridine derivatives were used to establish 3D/2D-QSAR models and to discuss the relationship between inhibitor structure and activity. Four ideal models were established, including the comparative molecular field analysis (CoMFA: [Formula: see text] = 0.574, [Formula: see text] = 0.947) model, comparative molecular similarity index analysis (CoMSIA: [Formula: see text]= 0.622, [Formula: see text] = 0.916) model, topomer CoMFA ([Formula: see text] = 0.691, [Formula: see text]= 0.912) model and hologram quantitative structure-activity relationship (HQSAR: [Formula: see text]= 0.759, [Formula: see text] = 0.963) model. They show quite good external predictive power for the test set, with [Formula: see text] values of 0.602, 0.624, 0.671 and 0.750, respectively. In addition, the contour and color code map given by the 2D/3D-QSAR model with the results of molecular docking analyzed to chalk up modification methods for improving inhibitory activity, which was verified by designing novel compounds. The analysis results are helpful to promote the modification of the inhibitor framework and to provide a reference for the construction of new and promising BRD4 inhibitor compounds.

The recent application of 3D-QSAR and docking studies to novel HIV-protease inhibitor drug discovery

INTRODUCTION

Despite the availability of FDA approved inhibitors of HIV protease, numerous efforts are still ongoing to achieve 'near-perfect' drugs devoid of characteristic adverse side effects, toxicities, and mutational resistance. While experimental methods have been plagued with huge consumption of time and resources, there has been an incessant shift towards the use of computational simulations in HIV protease inhibitor drug discovery.

AREAS COVERED

Herein, the authors review the numerous applications of 3D-QSAR modeling methods over recent years relative to the design of new HIV protease inhibitors from a series of experimentally derived compounds. Also, the augmentative contributions of molecular docking are discussed.

EXPERT OPINION

Efforts to optimize 3D QSAR and molecular docking for HIV-1 drug discovery are ongoing, which could further incorporate inhibitor motions at the active site using molecular dynamics parameters. Also, highly predictive machine learning algorithms such as random forest, K-means, decision trees, linear regression, hierarchical clustering, and Bayesian classifiers could be employed.

Computationally exploring novel xanthine oxidase inhibitors using docking-based 3D-QSAR, molecular dynamics, and virtual screening

Computationally exploring novel potential xanthine oxidase inhibitors using a systematic modeling study.

Drug design and molecular docking simulations of Polo-like kinase 1 inhibitors based on QSAR study

Computationally exploring novel potential Polo-like kinase 1 inhibitors using a systematic modeling study.

Design of dipicolinic acid derivatives as New Delhi metallo-β-lactamase-1 inhibitors using a combined computational approach

New Delhi metallo-β-lactamase (NDM-1) is the most recent addition to the class of metallo-β-lactamases (MBLs). This enzyme leads to antibiotic resistance in clinical treatments owing to its exertion of hydrolysis activity in almost all clinically available β-lactam antibiotics. Consequently, inhibitors targeting NDM-1 have attracted considerable research attention. However, progress has been slow regarding the study of the quantitative structure-activity relationship (QSAR) of NDM-1 inhibitors. In this study, a three-dimensional QSAR (3 D-QSAR) for NDM-1 inhibitors was established using Topomer CoMFA. The multiple correlation coefficients of the fitting model, leave-one-out cross validation, and external validation were found to be 0.761, 0.976, and 0.972, respectively. Topomer Search was used to design 16 new molecules that inhibit NDM-1 using R-group search from ZINC databases, 10 of which had comparatively high activities against NDM-1. The results indicate that Topomer CoMFA and Topomer Search can be used to design new NDM-1 inhibitors and guide the design of new NDM-1 drugs with good predictive capability. Furthermore, from molecular modeling and binding free-energy calculation, it was found that the newly designed molecules can bind to the catalytic region of NDM-1. Additionally, the newly designed inhibitors formed strong interactions with Ile35, Met67, Phe70, Trp93, His122, His189, Cys208, and His250 around the Zn²⁺-centered active region of NDM-1. These findings will facilitate the development of more effective NDM-1 inhibitors for use as potential antibacterial agents.Communicated by Ramaswamy H. Sarma.

Molecular Modeling and Design Studies of Purine Derivatives as Novel CDK2 Inhibitors

Cyclin-dependent kinase 2 (CDK2) is a potential target for treating cancer. Purine heterocycles have attracted particular attention as the scaffolds for the development of CDK2 inhibitors. To explore the interaction mechanism and the structure–activity relationship (SAR) and to design novel candidate compounds as potential CDK2 inhibitors, a systematic molecular modeling study was conducted on 35 purine derivatives as CDK2 inhibitors by combining three-dimensional quantitative SAR (3D-QSAR), virtual screening, molecular docking, and molecular dynamics (MD) simulations. The predictive CoMFA model (q² = 0.743, rpred2 = 0.991), the CoMSIA model (q² = 0.808, rpred2 = 0.990), and the Topomer CoMFA model (q² = 0.779, rpred2 = 0.962) were obtained. Contour maps revealed that the electrostatic, hydrophobic, hydrogen bond donor and steric fields played key roles in the QSAR models. Thirty-one novel candidate compounds with suitable predicted activity (predicted pIC₅₀ > 8) were designed by using the results of virtual screening. Molecular docking indicated that residues Asp86, Glu81, Leu83, Lys89, Lys33, and Gln131 formed hydrogen bonds with the ligand, which affected activity of the ligand. Based on the QSAR model prediction and molecular docking, two candidate compounds, I13 and I60 (predicted pIC₅₀ > 8, docking score > 10), with the most potential research value were further screened out. MD simulations of the corresponding complexes of these two candidate compounds further verified their stability. This study provided valuable information for the development of new potential CDK2 inhibitors.