Development of machine learning models for the screening of potential HSP90 inhibitors
Mohd Imran Khan1, Taehwan Park1, Mohammad Azhar Imran1
1Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Abstract:
Heat shock protein 90 (Hsp90) is a molecular chaperone playing a significant role in the folding of client proteins. This cellular protein is linked to the progression of several cancer types, including breast cancer, lung cancer, and gastrointestinal stromal tumors. Several oncogenic kinases are Hsp90 clients and their activity depends on this molecular chaperone. This makes HSP90 a prominent therapeutic target for cancer treatment. Studies have confirmed the inhibition of HSP90 as a striking therapeutic treatment for cancer management. In this study, we have utilized machine learning and different in silico approaches to screen the KCB database to identify the potential HSP90 inhibitors. Further evaluation of these inhibitors on various cancer cell lines showed favorable inhibitory activity. These inhibitors could serve as a basis for future development of effective HSP90 inhibitors.
Insights
Machine learning identified novel Heat Shock Protein 90 (Hsp90) inhibitors. These compounds show promising activity against cancer cell lines, offering a new avenue for cancer therapy development.
Area of Science:
- Molecular biology
- Oncology
- Computational chemistry
Background:
- Heat Shock Protein 90 (Hsp90) is a crucial molecular chaperone involved in protein folding.
- Hsp90 is implicated in the progression of various cancers, including breast, lung, and gastrointestinal stromal tumors.
- Oncogenic kinases, essential for cancer growth, rely on Hsp90 for their activity, making it a key therapeutic target.
Purpose of the Study:
- To identify potential Hsp90 inhibitors using machine learning and in silico methods.
- To screen the KCB database for novel compounds targeting Hsp90.
- To evaluate the inhibitory potential of identified compounds against cancer.
Main Methods:
- Utilized machine learning algorithms for screening.
- Employed in silico approaches for compound identification.
- Conducted in vitro evaluation on various cancer cell lines.
Main Results:
- Successfully screened the KCB database to identify potential Hsp90 inhibitors.
- Identified compounds demonstrated favorable inhibitory activity against tested cancer cell lines.
- The identified inhibitors show promise for further drug development.
Conclusions:
- Machine learning and in silico methods are effective for discovering Hsp90 inhibitors.
- The identified compounds represent a potential new class of anti-cancer therapeutics.
- Further research can build upon these findings for developing novel cancer treatments.
