Journal
MEDICINAL RESEARCH REVIEWS
Volume 41, Issue 3, Pages 1427-1473Publisher
WILEY
DOI: 10.1002/med.21764
Keywords
Alzheimer' s; anesthesia; artificial intelligence; blood‐ brain barrier; CNS; depression; disease subtyping; drug design; drug discovery; machine learning; neurological diseases; pain treatment; Parkinson' s; schizophrenia; target identification
Categories
Funding
- National Institutes of Health (NIH)/National Institute on Aging [R01AG057907, R01AG062355, R01AG068030, RF1AG054014, RF1AG057440, U01AG046170, U01AG052411, U01AG058635]
- Icahn School of Medicine at Mount Sinai Seed Fund
Ask authors/readers for more resources
Neurological disorders are more prevalent than diseases in other therapeutic areas, making drug discovery for CNS disorders a challenging task. The advancements in AI and ML have shown great potential to accelerate the drug discovery process in the CNS area, improving success rates.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available