QSAR, docking studies of 1,3-thiazinan-3-yl isonicotinamide derivatives for antitubercular activity

2,4,6-Trimethoxy chalcone derivatives: an integrated study for redesigning novel chemical entities as anticancer agents through QSAR, molecular docking, ADMET prediction, and computational simulation

QSAR, an efficient and successful approach for optimizing lead compounds in drug design, was employed to study a reported series of compounds derived from 2,4,6-trimethoxy chalcone derivatives. The ability of these compounds to inhibit CDK1 was examined, with the help of QSARINS software for model development. The generated QSAR model revealed three significant descriptors, exhibiting strong correlations with impressive statistical values: cross-validation leave-one-out correlation coefficient (Q2LOO) = 0.6663, coefficient of determination (R2) = 0.7863, external validation coefficient (R2ext) = 0.7854, cross-validation leave-many-out correlation coefficient (Q2LMO) = 0.6256, Concordance Correlation Coefficient for cross-validation (CCCcv) = 0.8150, CCCtr = 0.8804, and CCCext = 0.8750. From the key structural findings and the insights gained from the descriptors, ETA_dPsi_A, WTPT-5, and GATS7s, new lead molecules were designed. The designed molecules were then evaluated for their CDK1 inhibitory activity using the three-descriptor model developed in this study. To evaluate their drug likeliness, in-silico ADMET predictions were made using Schrodinger's Software. Molecular docking was carried out to determine the interactions of designed compounds with the target protein. The designed compounds having excellent binding pocket molecular stability and anticancer effectiveness was substantiated by the findings of the molecular dynamics simulation. The results of this work point out important properties and crucial interactions necessary for efficient protein inhibition, suggesting lead candidates for further development as novel anticancer agents.Communicated by Ramaswamy H. Sarma.

In-silico studies of 2-aminothiazole derivatives as anticancer agents by QSAR, molecular docking, MD simulation and MM-GBSA approaches

Targeting Hec1/Nek2 is considered as crucial target for cancer treatment due to its significant role in cell proliferation. In pursuit of this, a series of twenty-five 2-aminothiazoles derivatives, along with their Hec1/Nek2 inhibitory activities were subjected to QSAR studies utilizing QSARINS software. The significant three descriptor QSAR model was generated, showing noteworthy statistical parameters: a correlation coefficient of cross validation leave one out (Q2LOO) = 0.7965, coefficient of determination (R2) = 0.8436, (R2ext) = 0.6308, cross validation leave many out (Q2LMO) = 0.7656, Concordance Correlation Coefficient (CCCCV = 0.8875), CCCtr = 0.9151, and CCCext = 0.0.7241. The descriptors integral to generated QSAR model include Moreau-Broto autocorrelation, which represents the spatial autocorrelation of a property along the molecular graph's topological structure (ATSC1i), Moran autocorrelation at lag 8, which is weighted by charges (MATS8c) and RPSA representing the total molecular surface area. It was noted that these descriptors significantly influence Hec1/Nek2 inhibitory activity of 2-aminothiazoles derivatives. New lead molecules were designed and predicted for their Hec1/Nek2 inhibitory activity based on the developed three descriptor model. Further, the ADMET and Molecular docking studies were carried out for these designed molecules. The three molecules were selected based on their docking score and further subjected for MD simulation studies. Post-MD MM-GBSA analysis were also performed to predicted the free binding energies of molecules. The study helped us to understand the key interactions between 2-aminothiazoles derivatives and Hec1/Nek2 protein that may be necessary to develop new lead molecules against cancer.Communicated by Ramaswamy H. Sarma.

In search of HIV entry inhibitors using molecular docking, ADME and toxicity studies of some Thiazolidinone-Pyrazine derivatives against CXCR4 co-receptor

BACKGROUND

Entry inhibitors prevent binding of human immunodeficiency virus protein to the chemokine receptor CXCR4 and are used along with conventional anti-HIV therapy. They aid in restoring immunity and can prevent the development of HIV-TB co-infection.

AIM

In the present study various thiazolidinone-pyrazine derivatives earlier studied for NNRT inhibition activity were gauged for their entry inhibitor potential.

OBJECTIVE

Objective of the study is to perform molecular docking, ADME, toxicity studies of some Thiazolidinone-Pyrazine Derivatives as entry inhibitors targeting CXCR4 co-receptors.

METHODS

In-silico docking studies were performed using AutoDock Vina software and compounds were further studied for ADME and toxicity using SwissADME and pkCSM software respectively.

RESULTS

Taking into consideration the docking results, pharmacokinetic behaviour and toxicity profile four molecules (compound 1, 9, 11 and 16) have shown potential as entry inhibitors.

CONCLUSION

These compounds have shown potential as both NNRTI and entry inhibitors and hence can be used in management of immune compromised diseases like TB-HIV coinfection.

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.