Big data and artificial intelligence modeling for drug discovery

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Abstract

Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets. As a result, recently developed artificial intelligence approaches such as deep learning and relevant modeling studies provide new solutions to efficacy and safety evaluations of drug candidates based on big data modeling and analysis. The resulting models provided deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes. The relevant novel data mining, curation, and management techniques provided critical support to recent modeling studies. In summary, the new advancement of artificial intelligence in the big data era has paved the road to future rational drug development and optimization, which will have a significant impact on drug discovery procedures and, eventually, public health.

Original languageEnglish (US)
Pages (from-to)573-589
Number of pages17
JournalAnnual Review of Pharmacology and Toxicology
Volume60
DOIs
StatePublished - Jan 6 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Toxicology
  • Pharmacology

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