3D-QSAR, molecular docking, simulation dynamic and ADMET studies on new quinolines derivatives against colorectal carcinoma activity.

Cancer is the uncontrolled spread of abnormal cells that results in abnormal tissue growth in the affected organ. One of the most important organs is exposed to the growth of colon cancer cells, which start in the large intestine (colon) or the rectum. Several therapeutic protocols were used to treat different kinds of cancer. Recently, several studies have targeted tubulin and microtubules due to their remarkable prefoliation. Also, recent research shows that quinoline compounds have significant efficacy against human colorectal cancer. So, the present work investigated the potential of thirty quinoline compounds as tubulin inhibitors using computational methods. A 3D-QSAR approach using two contours (CoMFA and CoMSIA), molecular docking simulation to determine the binding type of the complexes (ligand-receptor), molecular dynamics simulation and identifying pharmacokinetic characteristics were used to design molecules. For all compounds designed (T1-5), molecular docking was used to compare the stability by type of binding. The ADMET has been utilized for molecules with good stability in molecular docking (T1-3); these compounds have good medicinal characteristics. Furthermore, a molecular dynamics simulation (MD) at 100 ns was performed to confirm the stability of the T1-3 compounds; the molecules (T1-3) remained the most stable throughout the simulation. The compounds T1, T2 and T3 are the best-designed drugs for colorectal carcinoma treatments.Communicated by Ramaswamy H. Sarma.

In silico identification of 1,2,4-triazoles as potential Candida Albicans inhibitors using 3D-QSAR, molecular docking, molecular dynamics simulations, and ADMET profiling.

Fluconazole and Voriconazole are individual antifungal inhibitors broadly adopted for treating fungal infections, including Candida Albicans. Unfortunately, these medicines clinically used have significant side effects. Consequently, the improvement of safer and better therapy became more indispensable. In this study, a set of 27 1,2,4-triazole compounds have been tested as potential Candida Albicans inhibitors by using different theoretical methods. The created comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) contour maps significantly impacted the development of novel Candida Albicans inhibitors with valuable activities. The mode of interactions between the 1,2,4-triazole inhibitors and the targeted receptor was studied by molecular docking simulation. The proposed new molecule P1 showed satisfied stability in the active pocket of the targeted receptor compared to the more active molecule in the dataset compared to Fluconazole medication. Meanwhile, the binding energy obtained by molecular docking for molecule P1 is - 9.3 kcal/mol compared with - 6.7 kcal/mol for Fluconazole medication. Also, MM/GBSA value obtained by molecular dynamics simulations at 100 ns for molecule P1 is - 33.34 kcal/mol compared with - 15.85 kcal/mol for Fluconazole medication. In addition, molecule P1 showed good oral bioavailability and was non-toxic according to ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. Therefore, the results indicated compound P1 might be a future inhibitor of Candida Albicans infection.

Prediction of potential inhibitors of SARS-CoV-2 using 3D-QSAR, molecular docking modeling and ADMET properties.

Coronavirus (COVID-19), an enveloped RNA virus, primarily affects human beings. It has been deemed by the World Health Organization (WHO) as a pandemic. For this reason, COVID-19 has become one of the most lethal viruses which the modern world has ever witnessed although some established pharmaceutical companies allege that they have come up with a remedy for COVID-19. To that end, a set of carboxamides sulfonamide derivatives has been under study using 3D-QSAR approach. CoMFA and CoMSIA are one of the most cardinal techniques used in molecular modeling to mold a worthwhile 3D-QSAR model. The expected predictability has been achieved using the CoMFA model (Q2 = 0.579; R2 = 0.989; R2test = 0.791) and the CoMSIA model (Q2 = 0.542; R2 = 0.975; R2test = 0.964). In a similar vein, the contour maps extracted from both CoMFA and CoMSIA models provide much useful information to determine the structural requirements impacting the activity; subsequently, these contour maps pave the way for proposing 8 compounds with important predicted activities. The molecular surflex-docking simulation has been adopted to scrutinize the interactions existing between potentially and used antimalarial molecule on a large scale, called Chloroquine (CQ) and the proposed carboxamides sulfonamide analogs with COVID-19 main protease (PDB: 6LU7). The outcomes of the molecular docking point out that the new molecule P1 has high stability in the active site of COVID-19 and an efficient binding affinity (total scoring) in relation with the Chloroquine. Last of all, the newly designed carboxamides sulfonamide molecules have been evaluated for their oral bioavailability and toxicity, the results point out that these scaffolds have cardinal ADMET properties and can be granted as reliable inhibitors against COVID-19.