Open AccessProteome-Informed Machine Learning Studies of Cocaine Addiction
Author(s) -
Kaifu Gao,
Dong Chen,
Alfred J. Robison,
Guo-Wei Wei
Publication year - 2021
Publication title -
the journal of physical chemistry letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.563
H-Index - 203
ISSN - 1948-7185
DOI - 10.1021/acs.jpclett.1c03133
Subject(s) - proteome , addiction , drug , computational biology , pharmacology , drug repositioning , repurposing , computer science , machine learning , chemistry , bioinformatics , medicine , biology , neuroscience , ecology
No anti-cocaine addiction drugs have been approved by the Food and Drug Administration despite decades of effort. The main challenge is the intricate molecular mechanisms of cocaine addiction, involving synergistic interactions among proteins upstream and downstream of the dopamine transporter. However, it is difficult to study so many proteins with traditional experiments, highlighting the need for innovative strategies in the field. We propose a proteome-informed machine learning (ML) platform for discovering nearly optimal anti-cocaine addiction lead compounds. We analyze proteomic protein-protein interaction networks for cocaine dependence to identify 141 involved drug targets and build 32 ML models for cross-target analysis of more than 60,000 drug candidates or experimental drugs for side effects and repurposing potentials. We further predict their ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. Our platform reveals that essentially all of the existing drug candidates fail in our cross-target and ADMET screenings but identifies several nearly optimal leads for further optimization.