In Silico Pharmacokinetic and Molecular Docking Studies of Natural Plants against Essential Protein KRAS for Treatment of Pancreatic Cancer
Marsha Mariya Kappan, Joby George

TL;DR
This study used computational methods to identify plant-derived compounds, especially borneol, as potential inhibitors of KRAS for pancreatic cancer treatment, combining molecular docking, machine learning, and ADME analysis.
Contribution
The paper introduces a novel in silico approach integrating molecular docking and machine learning to identify natural compounds targeting KRAS in pancreatic cancer.
Findings
Borneol showed strong binding affinity to KRAS.
Machine learning predicted borneol's bioactivity as promising.
ADME analysis supported borneol's drug-like properties.
Abstract
A kind of pancreatic cancer called Pancreatic Ductal Adenocarcinoma (PDAC) is anticipated to be one of the main causes of mortality during past years. Evidence from several researches supported the concept that the oncogenic KRAS (Ki-ras2 Kirsten rat sarcoma viral oncogene) mutation is the major cause of pancreatic cancer. KRAS acts as an on-off switch that promotes cell growth. But when the KRAS gene is mutated, it will be in one position, allowing the cell growth uncontrollably. This uncontrollable multiplication of cells causes cancer growth. Therefore, KRAS was selected as the target protein in the study. Fifty plant-derived compounds are selected for the study. To determine whether the examined drugs could bind to the KRAS complex's binding pocket, molecular docking was performed. Computational analyses were used to assess the possible ability of tested substances to pass the Blood…
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Taxonomy
TopicsComputational Drug Discovery Methods
