  Drug-target interaction (DTI) prediction has become a foundational task in
drug repositioning, polypharmacology, drug discovery, as well as drug
resistance and side-effect prediction. DTI identification using machine
learning is gaining popularity in these research areas. Through the years,
numerous deep learning methods have been proposed for DTI prediction.
Nevertheless, prediction accuracy and efficiency remain key challenges.
Pharmaco-electroencephalogram (pharmaco-EEG) is considered valuable in the
development of central nervous system-active drugs. Quantitative EEG analysis
demonstrates high reliability in studying the effects of drugs on the brain.
Earlier preclinical pharmaco-EEG studies showed that different types of drugs
can be classified according to their mechanism of action on neural activity.
Here, we propose a convolutional neural network for EEG-mediated DTI
prediction. This new approach can explain the mechanisms underlying complicated
drug actions, as it allows the identification of similarities in the mechanisms
of action and effects of psychotropic drugs.
