Ignition of Small Molecule Inhibitors in Friedreich's Ataxia with Explainable Artificial Intelligence

Iron (Fe) chelating medicines and Histone deacetylase (HDAC) inhibitors are two therapy options for hereditary Friedreich's Ataxia that have been shown to improve clinical results (FA). Fe chelation molecules can be used to minimize the quantity of stored Fe, and HDAC inhibitors can be used to boost the expression of the Frataxin (FXN) gene in the process of enhancing FA. A complete quantitative structure-activity relationship (QSAR) search of inhibitors from the ChEMBL database is reported in this paper, which includes 437 compounds for Fe chelation and 1,354 compounds for HDAC inhibitors. For further investigation, the IC50 was chosen as the unit of bioactivity, and following data refinement, a final dataset of 436 and 1,163 compounds for Fe chelation and HDAC inhibition, respectively, was produced. The Random Forest (RF) technique was used to generate models, and the models created using the PubChem fingerprint were the strongest of the 12 fingerprint kinds, hence that feature was chosen for interpretation. The results showed the importance of properties related to nitrogen-containing functional groups and aromatic rings. As a result, we explained the effect of the molecular fingerprints used on the models and therefore the effects on possible drugs that can be developed for FA with artificial intelligence (XAI), which can be explained through SHAP (Shapley Additive Explanations) values. Model scripts and fingerprinting methods are also available at https://github.com/tissueandcells/XAI.