HT_PREDICT: a machine learning-based computational open-source tool for screening HDAC6 inhibitors

ABSTRACT

Histone deacetylase 6 (HDAC6) is a promising drug target for the treatment of human diseases such as cancer, neurodegenerative diseases (in particular, Alzheimer’s disease), and multiple sclerosis. Considerable attention is paid to the development of selective non-toxic HDAC6 inhibitors. To this end, we successfully form a set of 3854 compounds and proposed adequate regression QSAR models for HDAC6 inhibitors. The models have been developed using the PubChem, Klekota-Roth, 2D atom pair fingerprints, and RDkit descriptors and the gradient boosting, support vector machines, neural network, and k-nearest neighbours methods. The models are integrated into the developed HT_PREDICT application, which is freely available at https://htpredict.streamlit.app/. In vitro studies have confirmed the predictive ability of the proposed QSAR models integrated into the HT_PREDICT web application. In addition, the virtual screening performed with the HT_PREDICT web application allowed us to propose two promising inhibitors for further investigations.

KEYWORDS:

• QSAR
• machine learning
• molecular fingerprints
• Streamlit
• selective inhibitors
• acute toxicity

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Part of this work was supported by the budget of the Institute of Physiologically Active Compounds of the Russian Academy of Sciences (IPAC RAS) State Targets – 2024 [topic No. FFSG-2024-0019].