Development of machine learning models for the screening of potential HSP90 inhibitors

Mohd Imran Khan; Taehwan Park; Mohammad Azhar Imran; Venu Venkatarame Gowda Saralamma; Duk Chul Lee; Jaehyuk Choi; Mohammad Hassan Baig; Jae-June Dong

doi:10.3389/fmolb.2022.967510

Development of machine learning models for the screening of potential HSP90 inhibitors

Front Mol Biosci. 2022 Oct 19:9:967510. doi: 10.3389/fmolb.2022.967510. eCollection 2022.

Authors

Mohd Imran Khan¹, Taehwan Park¹, Mohammad Azhar Imran¹, Venu Venkatarame Gowda Saralamma¹, Duk Chul Lee², Jaehyuk Choi³, Mohammad Hassan Baig¹, Jae-June Dong¹

Affiliations

¹ Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
² Department of Family Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
³ BNJBiopharma, Yonsei University International Campus, Incheon, 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.

Keywords: Hsp90; cancer; machine learing; pharmacophore; virtual screeening.