Modified oxymatrine as novel therapeutic inhibitors against Monkeypox and Marburg virus through computational drug design approaches

Global impact of viral diseases specially Monkeypox (mpox) and Marburg virus, emphasizing the urgent need for effective drug interventions. Oxymatrine is an alkaloid which has been selected and modified using various functional groups to enhance its efficacy. The modifications were evaluated using various computatioanal analysis such as pass prediction, molecular docking, ADMET, and molecular dynamic simulation. Mpox and Marburg virus were chosen as target diseases based on their maximum pass prediction spectrum against viral disease. After that, molecular docking, dynamic simulation, DFT, calculation and ADMET prediction were determined. The main objective of this study was to enhance the efficacy of oxymatrine derivatives through functional group modifications and computational analyses to develop effective drug candidates against mpox and Marburg viruses. The calculated binding affinities indicated strong interactions against both mpox virus and Marburg virus. After that, the molecular dynamic simulation was conducted at 100 ns, which confirmed the stability of the binding interactions between the modified oxymatrine derivatives and target proteins. Then, the modified oxymatrine derivatives conducted theoretical ADMET profiling, which demonstrated their potential for effective drug development. Moreover, HOMO-LUMO calculation was performed to understand the chemical reactivity and physicochemical properties of compounds. This computational analysis indicated that modified oxymatrine derivatives for the treatment of mpox and Marburg virus suggested effective drug candidates based on their binding affinity, drug-like properties, stability and chemical reactivity. However, further experimental validation is necessary to confirm their clinical value and efficacy as therapeutic candidates.

Unveiling promising phytocompounds from Moringa oleifera as dual inhibitors of EGFR(T790M/C797S) and VEGFR-2 in non-small cell lung cancer through in silico screening, ADMET, dynamics simulation, and DFT analysis

Non-small cell lung cancer (NSCLC) is among the main causes of mortality from cancer around the globe, affecting all genders. Current treatments mainly focus on tyrosine kinase inhibitors (TKIs) targeting the epidermal growth factor receptor (EGFR). However, resistance mechanisms, such as the emergence of T790M and C797S EGFR mutations and upregulation of VEGFR-2, often hinder the effectiveness of TKIs. Thereby, EGFR and VEGFR-2 present an intriguing opportunity for the treatment of NSCLC by developing dual-acting drugs. This research aims to evaluate prospective Moringa oleifera L. (MO)-originated compounds to efficiently block both of these receptors. In our research, we screened a library of 200 compounds sourced from MO, a plant known for its remarkable therapeutic potential. We identified five intriguing phytocompounds: hesperetin, gossypetin, quercetin, gallocatechin, and epigallocatechin, as potential anti-cancer agents. The compounds have demonstrated notable binding affinity in virtual screening and multi-stage molecular docking analysis, surpassing the controls, Erlotinib and Bevacizumab + Rituximab. In addition, these compounds demonstrate top-notch drug-likeness and ADMET properties. The five promising drug candidates also had a strong ability to bind to receptors and stayed stable with them during the 200 ns molecular dynamics (MD) simulation and MM-GBSA calculation. Furthermore, DFT analysis indicates that hesperetin, gossypetin, and quercetagetin stand out as the most promising drug candidates among all others. The findings of our study suggest that these three therapeutic candidates can precisely target both EGFR and VEGFR-2 and can potentially act on both of these pathways as a single agent.

Insights into the inhibitory potential of novel hydrazinyl thiazole-linked indenoquinoxaline against alpha-amylase: a comprehensive QSAR, pharmacokinetic, and molecular modeling study

The rising prevalence of diabetes necessitates the development of novel drugs, especially given the side effects associated with current medications like Acarbose and Voglibose. A series of 36 Hydrazinyl thiazole-linked indenoquinoxaline derivatives with notable activity against alpha-amylase were studied. To create a molecular model predicting alpha-amylase activity, a QSAR study was performed on these compounds. Molecular descriptors were calculated using Chem3D and Gaussian software and then correlated with their IC50 biological activities to form a dataset. This model data was refined using PCA and modeled with MLR. The model's performance was statistically verified (R2 =0.800; R adj 2 = 0.767; R cv 2 = 0.651) and its applicability domain was defined. It was predicted to possess high predictive power (R test 2   = 0.872). Based on this, new compounds were proposed, and their activities were predicted using the developed model. Additionally, their binding ability to the biological target was studied through molecular docking and dynamics. Their pharmacokinetics were also evaluated using ADMET predictions. Two designed compounds named AE and AB emerged as particularly promising, displaying properties that suggest substantial therapeutic potential and they can form stable complexes into the binding pocket of alpha-amylase enzyme.Communicated by Ramaswamy H. Sarma.

2-Amino Thiazole Derivatives as Prospective Aurora Kinase Inhibitors against Breast Cancer: QSAR, ADMET Prediction, Molecular Docking, and Molecular Dynamic Simulation Studies

The aurora kinase is a key enzyme that is implicated in tumor growth. Research revealed that small molecules that target aurora kinase have beneficial effects as anticancer agents. In the present study, in order to identify potential antibreast cancer agents with aurora kinase inhibitory activity, we employed QSARINS software to perform the quantitative structure-activity relationship (QSAR). The statistical values resulted from the study include R2 = 0.8902, CCCtr = 0.7580, Q2 LOO = 0.7875, Q2LMO = 0.7624, CCCcv = 0.7535, R2ext = 0.8735, and CCCext = 0.8783. Among the four generated models, the two best models encompass five important variables, including PSA, EstateVSA5, MoRSEP3, MATSp5, and RDFC24. The parameters including the atomic volume, atomic charges, and Sanderson's electronegativity played an important role in designing newer lead compounds. Based on the above data, we have designed six series of compounds including 1a-e, 2a-e, 3a-e, 4a-e, 5a-e, and 6a-e. All these compounds were subjected to molecular docking studies by using AutoDock v4.2.6 against the aurora kinase protein (1MQ4). Among the above 30 compounds, the 2-amino thiazole derivatives 1a, 2a, 3e, 4d, 5d, and 6d have excellent binding interactions with the active site of 1MQ4. Compound 1a had the highest docking score (-9.67) and hence was additionally subjected to molecular dynamic simulation investigations for 100 ns. The stable binding of compound 1a with 1MQ4 was verified by RMSD, RMSF, RoG, H-bond, molecular mechanics-generalized Born surface area (MM-GBSA), free binding energy calculations, and solvent-accessible surface area (SASA) analyses. Furthermore, newly designed compound 1a exhibited excellent ADMET properties. Based on the above findings, we propose that the designed compound 1a may be utilized as the best theoretical lead for future experimental research of selective inhibition of aurora kinase, therefore assisting in the creation of new antibreast cancer drugs.

CORAL: probing the structural requirements for α-amylase inhibition activity of 5-(3-arylallylidene)-2-(arylimino)thiazolidin-4-one derivatives based on QSAR with correlation intensity index, molecular docking, molecular dynamics, and ADMET studies

The present study aims to examine the structural requirements governing α-amylase inhibitory activity of 5-(3-arylallylidene)-2-(arylimino)thiazolidin-4-one derivatives and their precursors by employing a multifaceted approach combining in vitro and in silico studies. The in vitro assay findings revealed strong inhibitory effect of this class of compounds against α-amylase and compound 20 exhibited maximum percentage inhibition of 88.54 ± 0.69, 84.98 ± 0.40, 77.26 ± 0.75, 67.80 ± 0.54, and 62.93 ± 1.17 at 200, 100, 50, 25, and 12.5 µg mL-1, respectively. Multiple CORAL QSAR models were developed from the randomly distributed eight splits by employing two target functions (TF1, TF2 with WCII = 0.0 and = 0.3, respectively), and the quality of predictions by the produced models was validated with the help of various statistical parameters. The model M-4 (R2Val = 0.8799) and model M-11 (R2Val = 0.9064) were the leading models developed by using TF1 and TF2. We designed five new congeneric inhibitors (D-1 to D-5) by incorporating SMILES features positively correlating with the activity. Molecular docking experiments were carried out to confirm the binding of these new inhibitors with the biological receptor α-amylase (PDB ID: 7TAA). Furthermore, molecular dynamic simulations provided a thorough knowledge of the binding process by shedding insight into the dynamic behavior and stability of the ligand-receptor complex over time. The results of this study highlight the key structural characteristics needed for improved α-amylase inhibitory efficacy and provide a rational basis for the development of more effective inhibitors.Communicated by Ramaswamy H. Sarma.

In silico design of novel CDK2 inhibitors through QSAR, ADMET, molecular docking and molecular dynamics simulation studies

The present study aims to investigate about the quantitative structure-activity relationship (QSAR) of a series of Thiazole derivatives reported as anticancer agents (hepatocellular carcinoma), using principally the electronic descriptors calculated by the DFT method and by applying the multiple linear regression method. The developed model showed good statistical parameters (R2 = 0.725, R2adj = 0.653, MSE = 0.060, R2test = 0.827, Q2cv = 0.536). The energy EHOMO orbital, electronic energy (TE), shape coefficient (I), number of rotatable bonds (NROT), and index of refraction (n) were revealed to be the main descriptors influencing the anti-cancer activity. Further, new Thiazole derivatives have been designed and their activities and pharmacokinetic properties have been predicted using the validated QSAR model. The designed molecules were then assessed to molecular docking (MD), and molecular dynamic (MDs) simulation accompanied by the calculation of the binding affinity using MMPBSA script according to 100 ns a simulation trajectory, to study both their affinity and their stability towards CDK2 as a target protein for the cancer disease treatment. This research concluded with the identification of four new CDK2 inhibitors which are A1, A3, A5, and A6 showing good pharmacokinetic properties. The MDs results revealed that the newly designed compound A5 remained stable in the active center of the discovered CDK2 protein, indicating its potential as a novel inhibitor for the treatment of hepatocellular carcinoma. The current findings may eventually contribute to the development of robust CDK2 inhibitors in the future.Communicated by Ramaswamy H. Sarma.

Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern

INTRODUCTION

Modern drug discovery generally is accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed.

AREAS COVERED

One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future.

EXPERT OPINION

Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.

QSAR, molecular docking and ADMET properties in silico studies of novel 4,5,6,7-tetrahydrobenzo[D]-thiazol-2-Yl derivatives derived from dimedone as potent anti-tumor agents through inhibition of C-Met receptor tyrosine kinase

A quantitative structure-activity relationship (QSAR) study is performed on 48 novel 4,5,6,7-tetrahydrobenzo[D]-thiazol-2 derivatives as anticancer agents capable of inhibiting c-Met receptor tyrosine kinase. The present study is conducted using multiple linear regression, multiple nonlinear regression and artificial neural networks. Three QSAR models are developed after partitioning the database into two sets (training and test) via the k-means method. The obtained values of the correlation coefficients by the three developed QSAR models are 0.90, 0.91 and 0.92, respectively. The resulting models are validated by using the external validation, leave-one-out cross-validation, Y-randomization test, and applicability domain methods. Moreover, we evaluated the drug-likeness properties of seven selected molecules based on their observed high activity to inhibit the c-Met receptor. The results of the evaluation showed that three of the seven compounds present drug-like characteristics. In order to identify the important active sites for the inhibition of the c-Met receptor responsible for the development of cancer cell lines, the crystallized form of the Crizotinib-c-Met complex (PDB code: 2WGJ) is used. These sites are used as references in the molecular docking test of the three selected molecules to identify the most suitable molecule for use as a new c-Met inhibitor. A comparative study is conducted based on the evaluation of the predicted properties of ADMET in silico between the candidate molecule and the Crizotinib inhibitor. The comparison results show that the selected molecule can be used as new anticancer drug candidates.

QSAR Modelling of Peptidomimetic Derivatives towards HKU4-CoV 3CLpro Inhibitors against MERS-CoV

In this paper, we report the relationship between the anti-MERS-CoV activities of the HKU4 derived peptides for some peptidomimetic compounds and various descriptors using the quantitative structure activity relationships (QSAR) methods. The used descriptors were computed using ChemSketch, Marvin Sketch and ChemOffice software. The principal components analysis (PCA) and the multiple linear regression (MLR) methods were used to propose a model with reliable predictive capacity. The original data set of 41 peptidomimetic derivatives was randomly divided into training and test sets of 34 and 7 compounds, respectively. The predictive ability of the best MLR model was assessed by determination coefficient R2 = 0.691, cross-validation parameter Q2cv = 0.528 and the external validation parameter R2test = 0.794.

Structural characterization and QSAR modeling of 1,2,4-triazole derivatives as α-glucosidase inhibitors

In order to identify potential new drugs classified as alpha-Glucosidase inhibitors (AGIs), we used quantum chemical descriptors and QSAR modeling to predict the biological activity of triazole derivatives as AGIs.