The DOI matches its Crossref record; 471 of the 473 references it cites were found in a registry or an index.
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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Bartel (2004) · doi:10.1016/s0092-8674(04)00045-5 · Cited by 34,714
Sørlie, T., Perou, C. M., Tibshirani, R. J., Aas, T., Geisler, S. B., Johnsen, H., Hastie, T. J., Eisen, M. B., van de Rijn, M., Jeffrey, S. S., et al. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.191367098 · Cited by 11,506
Molecular Mechanisms of Depression: Perspectives on New Treatment Strategies
Lang, U. E., & Borgwardt, S. (2013) · Cellular Physiology and Biochemistry · doi:10.1159/000350094 · Cited by 8,526
Can the pharmaceutical industry reduce attrition rates?
Kola, I., & Landis, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1470 · Cited by 4,167
Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
Svetnik, V., Liaw, A. I., Tong, C., Culberson, J. C., Sheridan, R. P., & Feuston, B. P. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034160g · Cited by 3,613
MoleculeNet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018) · Chemical Science · doi:10.1039/c7sc02664a · Cited by 3,107
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106
Mechanisms of Alternative Pre-Messenger RNA Splicing
Black, D. L. (2003) · Annual Review of Biochemistry · doi:10.1146/annurev.biochem.72.121801.161720 · Cited by 2,704
A Deep Learning Approach to Antibiotic Discovery
Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.021 · Cited by 2,463
Schizophrenia: A Concise Overview of Incidence, Prevalence, and Mortality
McGrath, J., Saha, S., Chant, D., & Welham, J. (2008) · Epidemiologic Reviews · doi:10.1093/epirev/mxn001 · Cited by 2,280
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2 of the 473 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- Todeschini R, 2001, Books—handbook of molecular descriptors, Angew Chem, 40, 1977
- Matsumoto S, 2020, Extraction of protein dynamics information hidden in Cryo‐EM map using deep learning, bioRxiv
- DahlGE JaitlyN SalakhutdinovRMulti‐task neural networks for QSAR predictions.arXiv preprint arXiv:14061231.2014.
- 10.1038/s41563-019-0332-5 · DOI
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Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473
“Moreover, the number of startups in this field has also escalated and reached 230 by June 2020 [476].”
BackgroundArtificial Intelligence in Pharmaceutical Technology and Drug Delivery Design
Vora, L. K., Gholap, A. D., Jetha, K. L., Singh, T. R. R., Solanki, H. K., & Chavda, V. P. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15071916 · Cited by 1,193
The role of AI in drug discovery: challenges, opportunities, and strategies
Blanco-González, A., Cabezón, A., Seco-González, A., Conde-Torres, D., Antelo-Riveiro, P., Piñeiro, Á., & Garcia-Fandino, R. (2023) · Pharmaceuticals · doi:10.3390/ph16060891 · Cited by 690
Using artificial intelligence in academic writing and research: An essential productivity tool
Khalifa, M., & Albadawy, M. (2024) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2024.100145 · Cited by 580
AI-Driven Drug Discovery: A Comprehensive Review
Ferreira, F. J. N., & Carneiro, A. S. (2025) · ACS Omega · doi:10.1021/acsomega.5c00549 · Cited by 202
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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