Computational paradigm for advancing lung cancer drug discovery

Abstract

Lung cancer remainders one of the foremost causes of cancer-related impermanence worldwide. The availability of novel medicines for patients with lung cancer is restricted by the extremely lengthy timetables and high attrition rates of traditional drug discovery procedures. However, in silico drug discovery has emerged as a powerful and affordable way to identify potential treatments. This work offers well-structured paradigms for using virtual techniques to identify potential lung cancer treatments. The main concerns are virtual screening, target validation and identification, pharmacokinetic assessment, and molecular docking. The cost and time of drug development are reduced and a valuable platform for discovering novel drugs to treat lung cancer is produced by merging computational resources with proper methodologies. The current work explores the recent advancements, challenges, and possible future paths. Mann-Whitney U test says that the sampled data is different in distribution for molecular weight (MW), LogP, amount of H acceptors, and quantity of H donors for active and inactive molecules. Python tool has been utilized and identified that the CHEMBL4850929 (C31H31F2N7O4) molecule is a potential drug. It has pIC50 7.61, Lipinski values in terms of MW 603.63, LogP 3.36, amount of H donors 1, quantity of H acceptors 10.