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

TL;DR

This study employs machine learning and deep learning models informed by protein-protein interaction networks of DAT, SERT, and NET to identify potential drug repurposing candidates and leads for cocaine addiction treatment.

Contribution

It introduces a systematic AI-based protocol utilizing interactome networks and ML/DL models for anti-cocaine addiction drug discovery.

Findings

Analyzed 61 protein targets with inhibitor datasets.

Built ML/DL models for 115,407 inhibitors.

Identified potential drug candidates with favorable ADMET profiles.

Abstract

Cocaine addiction is a psychosocial disorder induced by the chronic use of cocaine and causes a large of number deaths around the world. Despite many decades' effort, no drugs have been approved by the Food and Drug Administration (FDA) for the treatment of cocaine dependence. Cocaine dependence is neurological and involves many interacting proteins in the interactome. Among them, dopamine transporter (DAT), serotonin transporter (SERT), and norepinephrine transporter (NET) are three major targets. Each of these targets has a large protein-protein interaction (PPI) network which must be considered in the anti-cocaine addiction drug discovery. This work presents DAT, SERT, and NET interactome network-informed machine learning/deep learning (ML/DL) studies of cocaine addiction. We collect and analyze 61 protein targets out 460 proteins in the DAT, SERT, and NET PPI networks that have sufficient existing inhibitor datasets. Utilizing autoencoder and other ML algorithms, we build ML/DL models for these targets with 115,407 inhibitors to predict drug repurposing potentials and possible side effects. We further screen their absorption, distribution, metabolism, and excretion, and toxicity (ADMET) properties to search for nearly optimal leads for anti-cocaine addiction. Our approach sets up a systematic protocol for artificial intelligence (AI)-based anti-cocaine addiction lead discovery.