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.

Emergent multipath COVID-19 specimen collection problem with green corridor through variable length GA.

The COVID-19 pandemic has spread worldwide exponentially. Typically, for testing, a provincial main government hospital cum testing center collects patients' specimens from remote health centers in the minimum possible time, satisfying the 'false negativity' constraint of the first collected specimen. With infrastructural developments throughout the world, multiple paths are available for transportation between two cities. Currently, the 'green corridor' is used for the transportation of human organs to be implanted, travel of VIPs, etc., in the minimum possible time. Taking these facts in consideration, for the first time, a green corridor system is suggested to provide a transportation pathway from small hospitals and urban/rural health centers to the testing center with COVID-19 specimens such as blood, nasal and throat swabs, and viral RNA, within the first collected specimen's life period. As health centers are located in different places, appropriate routing plans are needed for visiting them in the minimum possible time. A problem arises if this routing time exceeds the 'false negativity' of the first collected specimen. Thus, multipath COVID-19 specimen collection problems (MPC-19SCPs) are mathematically formulated to be collected from all health centers, and optimum routing plans are obtained using fixed and variable length genetic algorithms (VLGAs) developed for this purpose. For the first time, green corridor systems are suggested to incorporate the centers. The objectives of the models are, subject to the 'false-negative" constraint, minimization of the system time (Model A) and the green corridor time without or with mutual cooperation among the minimum number of centers for the transfer of specimens (Models B and C, respectively). The developed algorithms are based on variable length chromosomes, probabilistic selection, comparison crossover and generation-dependent mutation. Some benchmark instances from TSPLIB are solved by VLGA and GA. The competitiveness of VLGA is established through ANOVA. The models are numerically demonstrated, and some conclusions are derived.

Diverse Synthetic Approaches and Biological Activities of Lucrative Pyrimidine- Triazine Hybrid Derivatives: A Review.

Pyrimidine and Triazine are rewarding pharmacophores as seen from their presence in different naturally and synthetically occurring drug molecules. Hybridization is a functional concept used in drug design. This updated review encompasses various synthetic procedures that have been used to prepare molecular hybrids of Pyrimidine and Triazine, detailed structureactivity relationship, and molecular docking studies with patents granted. The most potent and promising hybrid compounds have also been identified. The study has revealed the synthetic feasibility of Pyrimidine-Triazine hybrids along with a plethora of potent biological activities such as anticonvulsant, antiviral, anti-inflammatory, analgesics, etc. This paper highlights the importance of coupling Pyrimidine and Triazine to provide better insight for medicinal chemists to further explore the hybrid for a significant therapeutic effect.