Exploring molecular docking algorithm for lung cancer drug discovery – a case study with ret protein

Abstract

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer-related deaths across the globe.1.33% of all NSCLC cases occur due to an alter-ation in RET protein. Commonly occurring RET fusion partners include KIF5B, CCDC6, NCOA4, and TRIM33. Numerous multikinase inhibitors are active against rearranged RET. However, mutations in the RET-fusion protein can result in adverse effects in terms of drug resistance against NSCLC. In this con-text, molecular docking algorithm is certainly important to support the drug discovery pipelines. However, availability of huge number of algorithms in the literature limits the researchers to proceed further in drug discovery develop-ment. Thus, the present study focuses on finding the best docking algorithm among ArgusLab, PatchDock, AutoDock 4.0 and AutoDock Vina for drug discovery process against RET fusion cancers using Pearson’s correlation coeffi-cient. We believe that our study will be a valuable source of information for carrying out further computational studies on RET fusion cancer, both mutant and wild type.