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Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions
Open accessAbstract
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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“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.”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [32]
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Metadata
- 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
- PMC
- PMC8043990
- 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
- hybrid
- doi.org
- Licence
- 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
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › 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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