CoMFA and CoMSIA studies on C-aryl glucoside SGLT2 inhibitors as potential anti-diabetic agents

In silico research on new sulfonamide derivatives as BRD4 inhibitors targeting acute myeloid leukemia using various computational techniques including 3D-QSAR, HQSAR, molecular docking, ADME/Tox, and molecular dynamics

Acute myeloid leukemia, a serious condition affecting stem cells, drives uncontrollable myeloblast proliferation, leading to accumulation. Extensive research seeks rapid, effective chemotherapeutics. A potential option is a BRD4 inhibitor, known for suppressing cell proliferation. Sulfonamide derivatives probed essential structural elements for potent BRD4 inhibitors. To achieve this goal, we employed 3D-QSAR molecular modeling techniques, including CoMFA, CoMSIA, and HQSAR models, along with molecular docking and molecular dynamics simulations. The validation of the 2D/3D QSAR models, both internally and externally, underscores their robustness and reliability. The contour plots derived from CoMFA, CoMSIA, and HQSAR analyses played a pivotal role in shaping the design of effective BRD4 inhibitors. Importantly, our findings highlight the advantageous impact of incorporating bulkier substituents on the pyridinone ring and hydrophobic/electrostatic substituents on the methoxy-substituted phenyl ring, enhancing interactions with the BRD4 target. The interaction mode of the new compounds with the BRD4 receptor (PDB ID: 4BJX) was investigated using molecular docking simulations, revealing favorable binding energies, supported by the formation of hydrogen and hydrophobic bonds with key protein residues. Moreover, these novel inhibitors exhibited good oral bioavailability and demonstrated non-toxic properties based on ADMET analysis. Furthermore, the newly designed compounds along with the most active one from series 58, underwent a molecular dynamics simulation to analyze their behavior. The simulation provided additional evidence to support the molecular docking results, confirming the sustained stability of the analyzed molecules over the trajectory. This outcome could serve as a valuable reference for designing and developing novel and effective BRD4 inhibitors.Communicated by Ramaswamy H. Sarma.

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.

In silico molecular investigations of pyridine N-Oxide compounds as potential inhibitors of SARS-CoV-2: 3D QSAR, molecular docking modeling, and ADMET screening

The new coronavirus SARS-CoV-2 virus is causing a severe pneumonia in human, provoking the serious outbreak epidemic CoV-2. Since its appearance in Wuhan, China on December 2019, CoV-2 becomes the biggest challenge the world is facing today, including the discovery of antiviral drug for SARS-CoV-2. In this study, the potential inhibitory of a class of human SARS inhibitors, namely pyridine N-oxide derivatives, against CoV-2 was addressed by quantitative structure-activity relationship 3 D-QSAR. The reliable CoMSIA developed model of 110 pyridine N-oxide based-antiviral compounds, showed Q²= 0.54 and rext2=0.71. The molecular surflex-docking was applied to identify the crystal structure of CoV-2 main protease 3CLpro (PDB: 6LU7) and two potentially and largely used antiviral molecules, namely chloroquine, hydroxychloroquine. The obtained free energy affinity and ADMET properties indicate that among the series of model antiviral compounds examined, the new antiviral compound A5 could be an excellent antiviral drug inhibitor against COVID-19. The inhibition activity of pyridine N-oxyde compounds against CoV-2 was compared with the activity of two common antiviral drug, namely chloroquine (CQ) and hydroxychloroquine (HCQ). DFT method was also used to define the sites of reactivity of pyridine N-oxyde derivatives as well as CQ and HCQ.Communicated by Ramaswamy H. Sarma.

A QSAR model for predicting antidiabetic activity of dipeptidyl peptidase-IV inhibitors by enhanced binary gravitational search algorithm

Time-varying binary gravitational search algorithm (TVBGSA) is proposed for predicting antidiabetic activity of 134 dipeptidyl peptidase-IV (DPP-IV) inhibitors. To improve the performance of the binary gravitational search algorithm (BGSA) method, we propose a dynamic time-varying transfer function. A new control parameter, μ , is added in the original transfer function as a time-varying variable. The TVBGSA-based model was internally and externally validated based on Qint2 , QLGO2 , QBoot2 , MSEtrain , Qext2 , MSEtest , Y-randomization test, and applicability domain evaluation. The validation results indicate that the proposed TVBGSA model is robust and not due to chance correlation. The descriptor selection and prediction performance of TVBGSA outperform BGSA method. TVBGSA shows higher Qint2 of 0.957, QLGO2 of 0.951, QBoot2 of 0.954, Qext2 of 0.938, and lower MSEtrain and MSEtest compared to obtained results by BGSA, indicating the best prediction performance of the proposed TVBGSA model. The results clearly reveal that the proposed TVBGSA method is useful for constructing reliable and robust QSARs for predicting antidiabetic activity of DPP-IV inhibitors prior to designing and experimental synthesizing of new DPP-IV inhibitors.

In Silico Exploration of Aryl Halides Analogues as Checkpoint Kinase 1 Inhibitors by Using 3D QSAR, Molecular Docking Study, and ADMET Screening

Purpose: In this review, a set of aryl halides analogs were identified as potent checkpoint kinase 1 (Chk1) inhibitors through a series of computer-aided drug design processes, to develop models with good predictive ability, highlight the important interactions between the ligand and the Chk1 receptor protein and determine properties of the new proposed drugs as Chk1 inhibitors agents.

Methods: Three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling, molecular docking and absorption, distribution, metabolism, excretion and toxicity (ADMET) approaches are used to determine structure activity relationship and confirm the stable conformation on the receptor pocket.

Results: The statistical analysis results of comparative -molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models that employed for a training set of 24 compounds gives reliable values of Q² (0.70 and 0.94, respectively) and R² (0.68 and 0.96, respectively).

Conclusion: Computer–aided drug design tools used to develop models that possess good predictive ability, and to determine the stability of the observed and predicted molecules in the receptor pocket, also in silico of pharmacokinetic (ADMET) results shows good properties and bioavailability for these new proposed Chk1 inhibitors agents.

QSAR studies in the discovery of novel type-II diabetic therapies

INTRODUCTION

Type-II diabetes mellitus (T2DM) is a complex chronic disease that represents a major therapeutic challenge. Despite extensive efforts in T2DM drug development, therapies remain unsatisfactory. Currently, there are many novel and important antidiabetic drug targets under investigation by many research groups worldwide. One of the main challenges to develop effective orally active hypoglycemic agents is off-target effects. Computational tools have impacted drug discovery at many levels. One of the earliest methods is quantitative structure-activity relationship (QSAR) studies. QSAR strategies help medicinal chemists understand the relationship between hypoglycemic activity and molecular properties. Hence, QSAR may hold promise in guiding the synthesis of specifically designed novel ligands that demonstrate high potency and target selectivity.

AREAS COVERED

This review aims to provide an overview of the QSAR strategies used to model antidiabetic agents. In particular, this review focuses on drug targets that raised recent scientific interest and/or led to successful antidiabetic agents in the market. Special emphasis has been made on studies that led to the identification of novel antidiabetic scaffolds.

EXPERT OPINION

Computer-aided molecular design and discovery techniques like QSAR have a great potential in designing leads against complex diseases such as T2DM. Combined with other in silico techniques, QSAR can provide more useful and rational insights to facilitate the discovery of novel compounds. However, since T2DM is a complex disease that includes several faulty biological targets, multi-target QSAR studies are recommended in the future to achieve efficient antidiabetic therapies.

3D-QSAR (CoMFA, CoMFA-RG, CoMSIA) and molecular docking study of thienopyrimidine and thienopyridine derivatives to explore structural requirements for aurora-B kinase inhibition

Aurora-B kinase plays a crucial role in cell cycle events and is identified as an important factor in regulation of spindle check point assembly. Thus, it can be proved as an important target in the field of oncology. 3D-QSAR model was generated using 54 molecules reported in literature containing thienopyrimidine and thienopyridine as scaffolds. All molecules were aligned using Distill function in Sybyl X1.2. This generated best model of CoMFA-RG (Region focusing) and CoMSIA were statistically significant with correlation coefficient r(2)ncv of 0.97, for both & Leave one out coefficient (LOO) q(2) of 0.70 and 0.72, respectively. Best CoMSIA model was built up using various combination of descriptors and proved statistical significant among all models. Best CoMFA-RG and CoMSIA models were validated by 12 test set molecules giving satisfactory prediction (r(2)pred) values of 0.86 and 0.88, respectively. External test set validation was performed using 20 molecules and satisfactory prediction of their biological activity was found. Active compounds were docked on protein (PDB ID: 4C2V) by GOLD module and revealed important interactions with amino acids at ATP-binding region. These data explored insight requirements for Aurora-B inhibition which might be fruitful for understanding mechanisms with kinase ligand interactions.