Proteome-Informed Machine Learning Studies of Cocaine Addiction.
Kai-Fu GaoDong ChenAlfred J RobisonGuo-Wei WeiPublished in: The journal of physical chemistry letters (2021)
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.
Keyphrases
- machine learning
- drug administration
- prefrontal cortex
- protein protein
- drug induced
- molecular docking
- small molecule
- high throughput
- artificial intelligence
- genome wide
- adverse drug
- deep learning
- big data
- oxidative stress
- dna methylation
- risk assessment
- metabolic syndrome
- molecular dynamics simulations
- climate change
- case control
- single cell