Data-Driven Based Methods to Design Anticancer Drugs to Target Kras

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Ahangarani, Danial | 2024

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 56938 (19)
University: Sharif University of Technology
Department: Computer Engineering
Advisor(s): fattahi, Alireza; Rohban, Mohammad Hossein
Abstract:
One of the goals of the current research is to investigate natural structures to inhibit Ras proteins that belong to the family of guanosine triphosphatase proteins. For example, one of the common mutations is Kras mutations, which are seen exclusively in pancreatic ductal adenocarcinoma. Changes in Kras protein expression are observed in 30 percent of lung cancer cases. Kras mutations occur in 35-45 percent of colon cancers, leading to drug resistance. Our work method in this research is that we collect a dataset of natural compounds for the target protein. Then, the binding energies of these structures with the receptor protein are calculated through Autodoc Vina. After that, using deep learning analyses, a model is designed to predict the binding energies
Keywords:
Machine Learning ; Drug Design ; Cancer ; Anticancer Drugs ; Natural Compounds