MT-EpiPred: Multitask Learning for Prediction of Small-Molecule Epigenetic Modulators

Epigenetic
modulators play an increasingly crucial role in the
treatment of various diseases. In this case, it is imperative to systematically
investigate the activity of these agents and understand their influence
on the entire epigenetic regulatory network rather than solely concentrate
on individual targets. This work introduces MT-EpiPred, a multitask
learning method capable of predicting the activity of compounds against
78 epigenetic targets. MT-EpiPred demonstrated outstanding performance,
boasting an average auROC of 0.915 and the ability to handle few-shot
targets. In comparison to the existing method, MT-EpiPred not only
expands the target pool but also achieves superior predictive performance
with the same data set. MT-EpiPred was then applied to predict the
epigenetic target of a newly synthesized compound (1),
where the molecular target was unknown. The method identified KDM4D
as a potential target, which was subsequently validated through an in vitro enzyme inhibition assay, revealing an IC50 of 4.8 μM. The MT-EpiPred method has been implemented in the
web server MT-EpiPred (http://epipred.com), providing free accessibility. In summary, this work presents a
convenient and accurate tool for discovering novel small-molecule
epigenetic modulators, particularly in the development of selective
inhibitors and evaluating the impact of these inhibitors over a broad
epigenetic network.