Artificial Intelligence Approach To Investigate the Longevity Drug.
Journal:
The journal of physical chemistry letters
PMID:
31411476
Abstract
Longevity is a very important and interesting topic, and has been demonstrated to be related to longevity. We combined network pharmacology, machine learning, deep learning, and molecular dynamics (MD) simulation to investigate potent lead drugs. Related protein insulin-like growth factor 1 receptor (IGF1R) and insulin receptor (IR) were docked with the traditional Chinese medicine (TCM) database to screen out several novel candidates. Besides, nine different machine learning algorithms were performed to build reliable and accurate predicted models. Moreover, we used the novel deep learning algorithm to build predicted models. All of these models obtained significant , which are all greater than 0.87 on the training set and higher than 0.88 for the test set, respectively. The long time 500 ns molecular dynamics simulation was also performed to verify protein-ligand properties and stability. Finally, we obtained , , and , which might be potent TCMs for two targets.
Authors
Keywords
Algorithms
Antigens, CD
Artificial Intelligence
Binding Sites
Databases, Factual
Drug Discovery
Glucuronidase
Inhibitory Concentration 50
Klotho Proteins
Ligands
Medicine, Chinese Traditional
Molecular Docking Simulation
Protein Binding
Protein Interaction Maps
Receptor, IGF Type 1
Receptor, Insulin
Signal Transduction
Thermodynamics