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.

NIMA related Kinases (NEK7) plays an important role in spindle assembly and mitotic division of the cell. Over expression of NEK7 leads to the progression of different cancers and associated malignancies. It is becoming the next wave of targets for the development of selective and potent anti-cancerous agents. The current study is the first comprehensive computational approach to identify potent inhibitors of NEK7 protein. For this purpose, previously identified anti-inflammatory compound i.e., Phenylcarbamoylpiperidine-1,2,4-triazole amide derivatives by our own group were selected for their anti-cancer potential via detailed Computational studies. Initially, the density functional theory (DFT) calculations were carried out using Gaussian 09 software which provided information about the compounds' stability and reactivity. Furthermore, Autodock suite and Molecular Operating Environment (MOE) software’s were used to dock the ligand database into the active pocket of the NEK7 protein. Both software performances were compared in terms of sampling power and scoring power. During the analysis, Autodock results were found to be more reproducible, implying that this software outperforms the MOE. The majority of the compounds, including M7, and M12 showed excellent binding energies and formed stable protein–ligand complexes with docking scores of − 29.66 kJ/mol and − 31.38 kJ/mol, respectively. The results were validated by molecular dynamics simulation studies where the stability and conformational transformation of the best protein–ligand complex were justified on the basis of RMSD and RMSF trajectory analysis. The drug likeness properties and toxicity profile of all compounds were determined by ADMETlab 2.0. Furthermore, the anticancer potential of the potent compounds were confirmed by cell viability (MTT) assay. This study suggested that selected compounds can be further investigated at molecular level and evaluated for cancer treatment and associated malignancies.

Butyrylcholinesterase is an acetylcholine-degrading enzyme involved in the memorization process, which is becoming an interesting target for the symptomatic treatment of Alzheimer's disease. In the present investigation, the structure–activity relationship of a set of Liquiritigenin derivatives recently revealed to be Butyrylcholinesterase inhibitors was studied basing on comparative field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMISA). As a result, performant models with high predictive capability have been developed (CoMFA model: R² = 0.91, Q² = 0.62, R²_pred = 0.85; CoMISA model: R² = 0.92, Q² = 0.59, R²_pred = 0.83) and implemented to design new Liquiritigenin derivatives with improved activity. Besides, the affinity of the designed derivatives towards the active site of Butyrylcholinesterase, was confirmed by molecular docking and molecular dynamics studies. Moreover, they exhibited good pharmacokinetics properties. Accordingly, the outcomes of the present investigations can provide important direction for the development of new anti-Alzheimer's drug candidates.

Main protease (Mpro) of SARS-CoV-2 is a key CoV enzyme that plays a pivotal role in mediating viral replication and transcription, making it an attractive drug target for SARS-CoV-2 the new strain of coronavirus. In this study, we evaluated biologically active compounds present in medicinal plants as potential SARS-CoV-2 Mpro inhibitors, using a molecular docking study with Autodock Vina software. Top seven compounds Afzelin, Phloroglucinol, Myricetin-3-O- rutinosid Tricin 7-neohesperidoside, Silybin, Kaempferol and Silychristin among 50 molecules of natural Origin (Algerian Medicinal plants) were selected which had better and significantly low binding energy as compared to the reference molecule with binding affinities of −9.3, −9.3, −9, −8.9, −8.5, 8.3 and −8.3 kcal mol⁻¹ respectively. Then, we analyzed the ADME properties of the best 7 ligands using the Web server SwissADME. Two of small molecules have been shown to be the ideal candidates for further drug development. Finally, the stability of the both compounds complexed with Mpro was validated through molecular dynamics (MD) simulation, they displayed stable trajectory (RMSD, RMSF) and molecular properties with consistent interaction profile in molecular dynamics simulations, moreover, Silybin could form more stable complex with Mpro than Silychristin. Communicated by Ramaswamy H. Sarma