Computational Drug Likeness Studies of Selected Thiosemicarbazones: A Sustainable Approach to Drug Design

The intake of drugs, their absorption in the body, their removal, and various side effects are factors that should be considered in drug design. Here, in silico tools act as virtual shortcuts assisting in the prediction of several important physicochemical properties like molecular weight, polar surface area (PSA), molecular flexibility, etc., to evaluate probable drug leads as potential drug candidates. Moreover, these tools also play an important role in the prediction of the bioactivity score of a probable drug lead against various human receptors. This paper presents a virtual combinatorial library of selected thiosmeicarbazone ligands and their metal complexes. Different properties, like physicochemical properties, bioactivity score, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters were assessed. The structures of ligands and complexes were drawn and downloaded in PDB format using ChemDraw Ultra 12.0. Physicochemical parameters were calculated using online software, viz., Molinspiration and SwissADME, and ADMET properties were calculated using admetSAR (2.0). Molecular docking was performed using PyRx Python Prescription 0.8. with two proteins, namely Transforming Growth Factor Beta (Tgf- β) and Janus Kinase. Transforming Growth Factor Beta (Tgf- β) and Janus Kinase are some of the cytokines involved in cell development, proliferation, and death. Salicyldehyde thiosemicarbazone, acenaphthenequinone thiosemicarbazone, and 2-chloronicotinic thiosemicarbazone and their virtually designed complexes exhibited appreciable in silico results. Most of the ligands and complexes had good bioactivity values against all the biological targets.