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Abstract Background The global landscape of public health faces significant challenges attributed to the prevalence of cancer and the emergence of treatment resistance. This study addresses these challenges by focusing on Cyclin-dependent Kinase 2 (CDK2) and employing a systematic computational approach for the discovery of novel cancer therapeutics. Results Initial ligand-based pharmacophore modelling, utilizing a training set of five reported CDK2 inhibitors, yielded a robust model characterized by Aro|Hyd| and |Acc|Don| features. Screening this validated model against the ZINC database identified 1881 hits, which were further subjected to molecular docking studies. The top 10 compounds (Z1–Z10) selected from the docking studies underwent Pharmacokinetic parameters Absorption, Distribution, Metabolism, Excretion and Toxicity profiling, Density Functional Theory (DFT) studies and the top two went for 100ns molecular dynamics (MD) simulations by comparing them with the standard Roscovitine. Compounds Z1 and Z2 emerged as the most promising, with docking scores of − 8.05 kcal/mol and − 8.02 kcal/mol, respectively. DFT analysis of the top 10 compounds revealed minimal variations in highest occupied molecular orbital–lowest unoccupied molecular orbital energy gaps, indicating consistent electronic stability and reactivity across the candidates. MD simulations of Z1 and Z2 confirmed their stable interactions with CDK2, with root mean square deviation (RMSD) values ranging from 1.4 to 2.5 Å for Z1 and 1.5 to 2.4 Å for Z2. Conclusion The current research identified compounds Z1 and Z2, which demonstrated significant potential as potent CDK2 inhibitors for cancer therapy, providing valuable insights into the development of more effective CDK2 inhibitors and addressing the critical need for innovative therapeutic strategies in cancer treatment. Graphical abstract

Using computational approaches, the potential efficacy and specificity of 1,3,4 trisubstituted pyrazole derivatives as MtInhA inhibitors which will aid in rational drug design for tubercular therapy were forecasted. QSARINS software was used to investigate the ability of compound to inhibit MtInhA. Three noteworthy descriptors with significant correlations and impressive statistical values were identified by the produced QSAR model: Correlation of coefficient(R2)= 0.8789, Cross-validation leave one out correlation coefficient (Q2LOO)= 0.8402, Cross-validation leave-many-out correlation coefficient(Q2LMO)=0.7321, Concordance Correlation Coefficient for cross-validation(CCCtr)=0.9355, CCCext =0.888. The descriptor generated by the QSAR model includes Centered Broto-Moreau autocorrelation weighted by Sanderson electronegativities (ATSC1e), Radial distribution functions at 15.0 and 2.0 Å inter-atomic distances weighted by relative van der Waals volumes (RDF150v), Radial distribution function – 145/weighted by relative I-state (RDF145s). Using these, three descriptor model was developed and the designed compounds were evaluated for their MtInhA inhibitory activity. Further, ADMET prediction and Molecular docking studies were carried out using Schrodinger's Software. ADMET prediction were used to evaluate drug likeliness and molecular docking was used to determine the interactions of designed compounds with the target protein. After the docking studies, the compounds were subjected for MM-GBSA calculations and MD simulation. Among the designed compounds, AP2 had the strongest binding affinity towards the MtInhA enzyme. The result of this work helps to understand the key interactions between 1,3,4 trisubstituted pyrazole derivatives and MtInhA protein that may be necessary to develop new lead compounds against tuberculosis.

Introduction: Recently, imidazooxazines have attracted more attention due to their therapeutic potential against tuberculosis (TB). The present study aimed to identify and develop potential inhibitors against Pks13-TE to combat the antimicrobial drug resistance of TB. Methods: Computer-aided drug design is a more highly valued technology than the traditional drug discovery approach. Herein, we computationally investigated a chemical dataset using QSAR models and virtually screened novel leads of imidazooxazines against the thioesterase domain, further subjecting them to molecular docking and dynamics simulation. Results: The present study identified two molecules, 1 and 3, promising leads with minimum energy conformations of −7.63 and −7.62 kcal, respectively, providing structural insight into Pks13 inhibition. The average values of MolSA, SASA, and PSA for molecules 1 and 3 were 382.41 Å, 77.65 Å, and 195.54 Å and 386.24 Å, 71.105 Å, and 184.46 Å, respectively. In conclusion, our research has demonstrated that imidazoxazines are promising leads to combat the resistance problem of TB. Among the two potent molecules 1 and 3, molecule 1 displayed favourable interactions in the active site with good stability, as confirmed by the RMSD, RMSF, RoG, H-bond, and SASA analyses. The Molecule 1 protein complex showed two strong hydrogen bonds, effectively maintained for 80–85 % of the simulation time, indicating its stability and potency. Conclusion: The identified two molecules and their conformations were highly stable; hence, these findings provide valuable insight into the evolution of new therapeutic agents to address the growing problem of TB and its resistance.

A series of novel 1,5-diaryl pyrazole derivatives targeting the COX enzyme were designed by combined ligand and structure-based approach. The designed molecules were then further subjected to ADMET and molecular docking studies. Out of 34 designed compounds, the top-10 molecules from the computation studies were synthesized, characterized, and evaluated for COX-2 inhibition and anti-cancer activity. Initially, the target compounds were screened for the protein denaturation assay. The results of the top-five molecules T2, T3, T5, T6, and T9 were further subjected to in vitro COX-2 enzymatic assay and anti-cancer activity. As far as COX-2 inhibitory activity is considered, two compounds, T3 and T5, exhibited the half maximum inhibitory concentration (IC50) at 0.781 µM and 0.781 µM respectively. Further, the two compounds T3 and T5, when evaluated for COX-1 inhibition, exhibited excellent inhibitory activity with T3 IC50 of 4.655μM and T5 with IC50 of 5.596 μM. The compound T5 showed more significant human COX-2 inhibition, with a selectivity index of 7.16, when compared with T3, which had a selectivity index of 5.96. Further, in vitro anti-cancer activity was screened against two cancer cell lines in which compounds T2 and T3 were active against A549 cell lines and T6 was active against the HepG2 cell line. Stronger binding energy was found by comparing MM-PBSA simulations with molecular docking, which suggests that compounds T3 and T5 have a better possibility of being effective compounds, in which T5 showed higher binding affinity. The results suggest that these compounds have the potential to develop effective COX-2 inhibitors as anti-cancer agents.