*Article* **Deep Learning and Structure-Based Virtual Screening for Drug Discovery against NEK7: A Novel Target for the Treatment of Cancer**

**Mubashir Aziz <sup>1</sup> , Syeda Abida Ejaz 1,\* , Seema Zargar <sup>2</sup> , Naveed Akhtar <sup>3</sup> , Abdullahi Tunde Aborode <sup>4</sup> , Tanveer A. Wani 5,\* , Gaber El-Saber Batiha <sup>6</sup> , Farhan Siddique 7,8 , Mohammed Alqarni <sup>9</sup> and Ashraf Akintayo Akintola <sup>10</sup>**

	- <sup>3</sup> Department of Pharmaceutics, Faculty of Pharmacy, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan; naveed.akhtar@iub.edu.pk
	- <sup>4</sup> Department of Chemistry, Mississippi State University, Starkville, MS 39759, USA; abdullahiaborodet@gmail.com

**Abstract:** NIMA-related kinase7 (NEK7) plays a multifunctional role in cell division and NLRP3 inflammasone activation. A typical expression or any mutation in the genetic makeup of NEK7 leads to the development of cancer malignancies and fatal inflammatory disease, i.e., breast cancer, non-small cell lung cancer, gout, rheumatoid arthritis, and liver cirrhosis. Therefore, NEK7 is a promising target for drug development against various cancer malignancies. The combination of drug repurposing and structure-based virtual screening of large libraries of compounds has dramatically improved the development of anticancer drugs. The current study focused on the virtual screening of 1200 benzene sulphonamide derivatives retrieved from the PubChem database by selecting and docking validation of the crystal structure of NEK7 protein (PDB ID: 2WQN). The compounds library was subjected to virtual screening using Auto Dock Vina. The binding energies of screened compounds were compared to standard Dabrafenib. In particular, compound **762** exhibited excellent binding energy of −42.67 kJ/mol, better than Dabrafenib (−33.89 kJ/mol). Selected drug candidates showed a reactive profile that was comparable to standard Dabrafenib. To characterize the stability of protein–ligand complexes, molecular dynamic simulations were performed, providing insight into the molecular interactions. The NEK7–Dabrafenib complex showed stability throughout the simulated trajectory. In addition, binding affinities, pIC50, and ADMET profiles of drug candidates were predicted using deep learning models. Deep learning models predicted the binding affinity of compound **762** best among all derivatives, which supports the findings of virtual screening. These findings suggest that top hits can serve as potential inhibitors of NEK7. Moreover, it is recommended to explore the inhibitory potential of identified hits compounds through in-vitro and in-vivo approaches.

**Citation:** Aziz, M.; Ejaz, S.A.;

Zargar, S.; Akhtar, N.; Aborode, A.T.; A. Wani, T.; Batiha, G.E.-S.; Siddique, F.; Alqarni, M.; Akintola, A.A. Deep Learning and Structure-Based Virtual Screening for Drug Discovery against NEK7: A Novel Target for the Treatment of Cancer. *Molecules* **2022**, *27*, 4098. https://doi.org/10.3390/ molecules27134098

Academic Editor: Peng Zhan

Received: 26 May 2022 Accepted: 18 June 2022 Published: 25 June 2022

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**Keywords:** NEK7; virtual screening; DFTs; deep learning; molecular dynamics; drug design; drug repurposing; structural-based; cancer
