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Exploring AI-Driven Drug Repurposing Strategies Targeting ErbB Signalling Pathway for Brain Cancer Therapy

Lookup NU author(s): Chi Ng, Jianqiao Long, Dr Jichun Li

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Abstract

© 2024 IEEE. This study explores the application of machine learning methodologies in drug repurposing for brain cancer therapy, focusing on targeting the epidermal growth factor receptor (EGFR). Our approach involved the development of a predictive model to estimate the half-maximal inhibitory concentration (IC50) values of compounds against EGFR, leveraging existing biological activity data of known EGFR inhibitors and molecular structure descriptors. The constructed model exhibited efficacy in predicting the inhibitory activity of compounds against EGFR. Subsequent screening of a library of known drugs using the predictive model led to the identification of several compounds with low predicted IC50 values, indicating their potential as drug candidates for further investigation. This study underscores the utility of integrating machine learning techniques into drug repurposing endeavours, offering a pragmatic approach to identifying potential therapeutic options for brain cancer treatment.

Publication metadata

Author(s): Ng CK, Long J, Tang M, Li J, Quan M

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: IEEE International Conference on Cybernetics and Intelligent Systems (CIS 2024) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM 2024)

Year of Conference: 2024

Pages: 555-560

Online publication date: 16/09/2024

Acceptance date: 02/04/2018

ISSN: 2326-8239

Publisher: IEEE

URL: https://doi.org/10.1109/CIS-RAM61939.2024.10673264

DOI: 10.1109/CIS-RAM61939.2024.10673264

Library holdings: Search Newcastle University Library for this item

ISBN: 9798350364194

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