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Ligand-based pharmacophore modelling in search of novel anaplastic lymphoma kinase inhibitors

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Gajulapalli, V. Pratap Reddy; Lee, Ju Yong; Sohn, In Suk

Issue Date
Results in Chemistry, Vol.5, p. 100752
Non-small-cell lung cancer (NSCLC) accounts for 85 % of lung cancer cases and is the leading cause of mortality globally. Anaplastic lymphoma kinase (ALK) has been identified as an NSCLC mutational cancer mediator. Many additional ALK inhibitors are currently undergoing clinical studies. Many ALK inhibitors have already been approved. Even though the discovery of ALK inhibitors revolutionized the treatment of NSCLC, it has been found that tumors recur due to drug resistance, prompting the development of innovative therapies. Highly selective inhibitors are also required because many approved inhibitors interact with several kinases. Using computational approaches to create novel, potent ALK inhibitors and knowledge of the ALK-approved drugs is a promising strategy for treating ALK-dependent cancers. This study focuses on developing a ligand-based pharmacophore model using ALK-approved drugs to virtual screen the CHEMBL and ZINC databases to find potential ALK in-hibitors. The top five complexes from the molecular docking approach are chosen for additional molecular dynamics simulation research. Three of the five compounds, CHEMBL1906779, ZINC74454057, and ZINC13162945, are stable throughout a 50 ns time period, according to simulation data. This suggests that they could be used to develop lead molecules with further optimization and experimental testing. It should be highlighted that the pharmacophore model developed in this study might potentially be helpful for bigger-scale initiatives involving virtual screening and de novo drug generation.
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  • Graduate School of Convergence Science & Technology
  • Dept. of Molecular and Biopharmaceutical Sciences
Research Area AI models for drug discovery, Free energy calculation, Molecular dynamics, 분자동역학, 신약개발을 위한 AI 모델, 자유에너지 계산


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