**4. Conclusions**

In the current study, structure-based virtual screening of a 1200-compound library and Dabrafenib was carried out using Auto Dock Vina. These compounds are in the early stages of drug development, and the in-silico approach used in this study was contributing toward investigating the inhibiting potential of these compounds through molecular docking, DFTs, and MD simulation, as well as determining the drug-like properties of these compounds through deep learning models. The FDA-approved drug, Dabrafenib, was considered as a standard drug to which in-silico findings could be compared. SBVS findings discovered four important hits having better binding energies as compared to standard Dabrafenib. In addition, the chemical reactivity profiles of top hits were determined via DFT studies. Findings from DFT studies revealed the reactive nature of the compounds. Moreover, the current study has utilized deep learning models for prediction of binding affinity, pIC50, and ADMET properties. It was observed that compound **762** showed good binding affinity and demonstrated a promising ADMET profile. Moreover, molecular dynamics

simulations were performed to determine the stability of the protein–ligand complex under accelerated conditions. It was observed that the ligand remained significantly attached to the protein-activation loop, suggesting potential inhibiting activity of the compound. In short, the findings of the current study identify top hits that could prove an effective treatment strategy for NEK7-associated cancer malignancies. These findings will assist researchers to develop newer leads without consuming much time and money. Further experimental studies are also recommended for future prospects.

**Author Contributions:** Conceptualization, M.A. (Mubashir Aziz), S.A.E. and T.A.W.; methodology, M.A. (Mubashir Aziz), F.S., M.A. (Mohammed Alqarni) and A.A.A.; software, G.E.-S.B.; validation, M.A. (Mubashir Aziz) and S.A.E.; formal analysis, S.Z. and G.E.-S.B.; investigation, M.A. (Mubashir Aziz), A.T.A. and T.A.W.; resources, M.A. (Mubashir Aziz), G.E.-S.B., M.A. (Mohammed Alqarni) and A.A.A.; data curation, F.S.; writing—original draft preparation, M.A. (Mubashir Aziz) and S.A.E.; writing—review and editing, M.A. (Mubashir Aziz), S.A.E., N.A. and T.A.W.; visualization, S.A.E.; supervision, S.A.E.; project administration, S.A.E.; funding acquisition, T.A.W. and S.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by King Saud University, Riyadh Saudi Arabia project number (RSP-2021/357).

**Institutional Review Board Statement:** Not Applicable.

**Informed Consent Statement:** Not Applicable.

**Data Availability Statement:** Not Applicable.

**Acknowledgments:** The authors extend their appreciation to researchers supporting project number (RSP-2021/357), King Saud University, Riyadh Saudi Arabia for funding this research.

**Conflicts of Interest:** The authors declare no conflict of interest.

**Sample Availability:** Not Applicable.
