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Article overview
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Machine learning analysis of cocaine addiction informed by DAT, SERT, and NET-based interactome networks | | Hongsong Feng
; Kaifu Gao
; Dong Chen
; Alfred J Robison
; Edmund Ellsworth
; Guo-Wei Wei
; | | Date: |
1 Jan 2022 | | 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. | | Source: | arXiv, 2201.00114 | | Services: | Forum | Review | PDF | Favorites | |
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