Development of a Simulation Environment for the Importance of Histone Deacetylase in Childhood Acute Leukemia with Explainable Artificial Intelligence

Ilhan Uysal, Utku Kose


This study aims to explore new therapeutic opportunities for histone deacetylase (HDAC) inhibitors by leveraging drug repurposing approaches and analyzing their bioactivity and molecular fingerprints. The methodology includes investigating drug repurposing opportunities for HDAC inhibitors, evaluating the bioactivity of repurposing drugs on HDAC enzymes, investigating the role of HDAC genes in therapeutic effects, and analyzing molecular fingerprints with explainable artificial intelligence (XAI) to identify structurally similar compounds with potential HDAC inhibitory activity. In this context, chemical compounds with IC50 (7903 compounds) and Inhibition (1084 compounds) standard types of HDAC genes reported to be associated with childhood acute leukemia were represented by molecular fingerprints. Regression and classification models were applied to the molecular fingerprints, and the results obtained were supported by XAI. All the study results were shared interactively on the website address by designing a simulation environment. The influence of molecular fingerprints on the models and their impact on potential drug development in childhood acute leukemia were evaluated using XAI techniques, particularly through the analysis of SHAP values. The study contributes to the literature on the use of XAI technology in drug repurposing studies, especially in cancer, the study of molecular properties, and the active use of XAI in drug repurposing studies.


Explainable artificial intelligence, childhood acute leukaemia, histone deacetylase, regression, classification.

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