Machine Learning Analysis of Cocaine Addiction Informed by DAT, SERT, and NET-Based Interactome Networks

Abstract

Cocaine addiction is a psychosocial disorder induced by the chronic use of cocaine and causes a large number of deaths around the world. Despite decades of 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, the dopamine (DAT), serotonin (SERT), and norepinephrine (NET) transporters are three major targets. Each of these targets has a large protein−protein interaction (PPI) network, which must be considered in the anticocaine addiction drug discovery. This work presents DAT, SERT, and NET interactome network-informed machine learning/deep learning (ML/DL) studies of cocaine addiction. We collected and analyzed 61 protein targets out of 460 proteins in the DAT, SERT, and NET PPI networks that have sufficiently large existing inhibitor datasets. Utilizing autoencoder (AE) and other ML/DL algorithms, including gradient boosting decision tree (GBDT) and multitask deep neural network (MT-DNN), we built predictive models for these targets with 115 407 inhibitors to predict drug repurposing potential and possible side effects. We further screened their absorption, distribution, metabolism, and excretion, and toxicity (ADMET) properties to search for leads having potential for developing treatments for cocaine addiction. Our approach offers a new systematic protocol for artificial intelligence (AI)-based anticocaine addiction lead discovery.

TIDAL: Topology-Inferred Drug Addiction Learning

“…The predictive results in terms of another metric, the squared Pearson correlation coefficient ( P 2 ), are given in Table S5 of the Supporting Information. More comparison with the results in the literature can be found in Table and is discussed in the next section.…”

Machine Learning Methods for Small Data Challenges in Molecular Science

“…Workflow of an autoencoder-assisted multitask ANN model for enhancing small data sets inferred by interactomics networks of cocaine addition targets . (a) Sequence-to-sequence autoencoder model is used to create uniform features for different data sets.…”

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“…When serotonin and norepinephrine get released, they begin to stimulate the receptors and at the same time, they are transported from the synapse back to their neurons in a process called reuptake. Serotonin is reabsorbed by serotonin transporter (SERT) while norepinephrine is reabsorbed by norepinephrine transporter (NET) [ 264 , 265 ]. Once the serotonin and norepinephrine get reabsorbed back to their neurons they are partially repackaged into synaptic vesicles and partially broken down into inactive metabolites by an enzyme monoamine oxidase (MAO).…”