Cited by 430Computational Drug Discovery MethodsMachine Learning in Materials ScienceArtificial Intelligence in Healthcare and Education

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

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Vatansever, Sezen, Schlessinger, Avner, Wacker, Daniel, Kaniskan, H. Ümit, Jin, Jian, Zhou, Ming‐Ming, Zhang, Bin·2021·Medicinal Research Reviews

Abstract

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

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  • AI-based methods can accelerate the drug discovery process and enable the development of more effective medications.

    “Many more examples are available [3,32,33,34,35,36,37], showing that AI-based methods can accelerate the drug discovery process and enable the development of more effective medications.”

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  1. [6]Todeschini R, Wiese M, Consonni V. Books—handbook of molecular descriptors. Angew Chem. 2001; 40(10): 1977.
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Authors
Vatansever, S., Schlessinger, A., Wacker, D., Kaniskan, H. Ü., Jin, J., Zhou, M., & Zhang, B.
Affiliations
  • Icahn School of Medicine at Mount Sinai (US)
  • Department of Pharmacological Sciences Icahn School of Medicine at Mount Sinai New York New York USA
  • Tisch Cancer Institute (US)
Corresponding author
Zhang, Bin
Venue
Medicinal Research Reviews
Publisher
Wiley
Published
2020-12-09
Volume
41
Issue
3
Pages
1427-1473
ISSN
  • 0198-6325 (ISSN-L)
  • 1098-1128
OpenAlex
W3112951420
Cited by
429
References
473
Citations by year
2026: 64 · 2025: 126 · 2024: 127 · 2023: 62 · 2022: 34 · 2021: 16
FWCI
2.48
Type
journal article
Language
en
Open access
Locations
  • Medicinal Research Reviews (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health
  • National Institute on Aging (U01AG058635, R01AG057907, U01AG046170, RF1AG054014, R01AG068030, RF1AG057440, RF1AG059319, R01AG062355, U01AG052411, R01AG072562)
  • National Institute of General Medical Sciences (R35GM133504)
  • National Institute of Allergy and Infectious Diseases (R01AI124465)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
artificial intelligence in drug discovery, machine learning, central nervous system disorders, neurological disorders, target identification, compound screening, drug synergy prediction, de novo drug design, drug repurposing, drug attrition
MeSH terms
Machine Learning, Algorithms, Artificial Intelligence, Central Nervous System Diseases, Humans, Drug Discovery

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