N. R. Das¹, P. G. R. Achary²*

¹Department of CSIT, Siksha ‘O’ Anusandhan deemed to be University, Bhubaneswar, Odisha, India;
²Department of Chemistry, Faculty of Engineering and Technology (ITER), Siksha ‘O’ Anusandhan deemed to be University, Bhubaneswar, Odisha, India;
*e-mail: pgrachary@soa.ac.in

Received: 28 April 2021; Accepted: 12 November 2021

Arachidonate 5-lipoxygenase (ALOX5) is considered a prime target for drug discovery in the area of liver fibrosis, rheumatoid arthritis, atherosclerosis, cancer and asthma. To date, the lead rate in the discovery of drugs that inhibit ALOX5 for the treatment of the above diseases is not satisfactory. So, the development of powerful and effective ALOX5-targeted drugs is desired. In this regard, Quantitative Structure-Activity Relationship (QSAR) and molecular docking can have a major role in screening and designing drugs. In this work, 3D-QSAR models were proposed, which were built using the techniques like Multiple Linear Regression (MLR), and Partial Least Squares (PLS) for the pEC50(M) taking a diverse dataset of 112 molecules. The technique of the ‘Index of Ideality of Correlation (IIC)’ was also investigated to generate an optimal descriptor derived from the SMILES molecular structure. The effect of the number and nature of descriptors on the model were analyzed. The models can be helpful in providing better directions for the development of novel drug targets for 5-lipoxygenase. A significant improvement in the stability of the model was observed by the incorporation of the optimal descriptor. The molecular docking results showed that the ALOX5 receptor was well inhibited by the 112 ligands showing the least binding energy (-10.8 Kcal/mol).  In order to validate the binding mode of the ligands docked with AutoDock Vina software, the top-scored compounds were re-docked using DockThor online docking server. The results obtained from docking suggest that the ligands with IDs 18, 20, 24, 30 and 44 are some of the potential inhibitors for ALOX5.