Skip to content
2000
Volume 22, Issue 15
  • ISSN: 1568-0266
  • E-ISSN: 1873-4294

Abstract

Objectives: The present work reviews current evidence regarding the contribution of machine learning to the discovery of new drug targets. Methods: Scientific articles from PubMed, SCOPUS, EMBASE, and Web of Science Core Collection published until May 2021 were included in this review. Results: The most significant areas of research are schizophrenia, depression and anxiety, Alzheimer´s disease, and substance use disorders. ML techniques have pinpointed target gene candidates and pathways, new molecular substances, and several biomarkers regarding psychiatric disorders. Drug repositioning studies using ML have identified multiple drug candidates as promising therapeutic agents. Conclusion: Next-generation ML techniques and subsequent deep learning may power new findings regarding the discovery of new pharmacological agents by bridging the gap between biological data and chemical drug information.

Loading

Article metrics loading...

/content/journals/ctmc/10.2174/1568026621666211004095917
2022-06-01
2025-06-14
Loading full text...

Full text loading...

/content/journals/ctmc/10.2174/1568026621666211004095917
Loading
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test