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Cocaine addiction is a psychosocial disorder induced by the chronic use ofcocaine and causes a large of number deaths around the world. Despite manydecades' effort, no drugs have been approved by the Food and DrugAdministration (FDA) for the treatment of cocaine dependence. Cocainedependence is neurological and involves many interacting proteins in theinteractome. Among them, dopamine transporter (DAT), serotonin transporter(SERT), and norepinephrine transporter (NET) are three major targets. Each ofthese targets has a large protein-protein interaction (PPI) network which mustbe considered in the anti-cocaine addiction drug discovery. This work presentsDAT, SERT, and NET interactome network-informed machine learning/deep learning(ML/DL) studies of cocaine addiction. We collect and analyze 61 protein targetsout 460 proteins in the DAT, SERT, and NET PPI networks that have sufficientexisting inhibitor datasets. Utilizing autoencoder and other ML algorithms, webuild ML/DL models for these targets with 115,407 inhibitors to predict drugrepurposing potentials and possible side effects. We further screen theirabsorption, distribution, metabolism, and excretion, and toxicity (ADMET)properties to search for nearly optimal leads for anti-cocaine addiction. Ourapproach sets up a systematic protocol for artificial intelligence (AI)-basedanti-cocaine addiction lead discovery.

Machine learning analysis of cocaine addiction informed by DAT, SERT, and NET-based interactome networks