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Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease
Cheng F, Wang F, Tang J, Zhou Y, Fu Z, Zhang P, Haines JL, Leverenz JB et al.. Cell reports. Medicine, 2024-02-01
Topic: Amyloid, tau and blood-test diagnosis
The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack of understanding of the pathophysiology of disease, and this deficit may be addressed by applying artificial intelligence (AI) to "big data" to rapidly and effectively expand therapeutic development efforts. Recent accelerations in computing power and availability of big data, including electronic health records and multi-omics profiles, have converged to provide opportunities for scientific discovery and treatment development. Here, we review the potential utility of applying AI approaches to big data for discovery of disease-modifying medicines for AD/ADRD. We illustrate how AI tools can be applied to the AD/ADRD drug development pipeline through collaborative efforts among neurologists, gerontologists, geneticists, pharmacologists, medicinal chemists, and computational scientists. AI and open data science expedite drug discovery and development of disease-modifying therapeutics for AD/ADRD and other neurodegenerative diseases.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Cheng, F., Wang, F., Tang, J., Zhou, Y., Fu, Z., Zhang, P., et al. (2024). Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease. Cell reports. Medicine. https://doi.org/10.1016/j.xcrm.2023.101379
Vancouver
Cheng F, Wang F, Tang J, Zhou Y, Fu Z, Zhang P, et al. Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease. Cell reports. Medicine. 2024. doi:10.1016/j.xcrm.2023.101379. PMID: 38382465.
BibTeX
@article{cheng2024-38382465,
author = {Cheng, F. and Wang, F. and Tang, J. and Zhou, Y. and Fu, Z. and Zhang, P. and Haines, J. L. and Leverenz, J. B. and others},
title = {{Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease}},
journal = {Cell reports. Medicine},
year = {2024},
doi = {10.1016/j.xcrm.2023.101379},
pmid = {38382465},
url = {https://doi.org/10.1016/j.xcrm.2023.101379}
}
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Key findings
- The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack of understanding of the pathophysiology of disease, and this deficit may be addressed by applying artificial intelligence (AI) to "big data" to rapidly and effectively expand therapeutic development efforts.
- Recent accelerations in computing power and availability of big data, including electronic health records and multi-omics profiles, have converged to provide opportunities for scientific discovery and treatment development.
- Here, we review the potential utility of applying AI approaches to big data for discovery of disease-modifying medicines for AD/ADRD.
- We illustrate how AI tools can be applied to the AD/ADRD drug development pipeline through collaborative efforts among neurologists, gerontologists, geneticists, pharmacologists, medicinal chemists, and computational scientists.
- AI and open data science expedite drug discovery and development of disease-modifying therapeutics for AD/ADRD and other neurodegenerative diseases.
Why it matters
This review argues that applying artificial intelligence to large datasets like electronic health records and multi-omics profiles could speed up discovery of disease-modifying medicines for Alzheimer's disease. It does not report any new treatment or cure, only a proposed approach to drug development.
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Study details
- Evidence strengthLow
How this study scored 50/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Cell reports. Medicine) |
| Journal's citation record | 1 / 4 | Cell Reports Medicine: journal h-index 104 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2024 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 59 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 14.8 citations a year over 4 year(s) since 2024 |
| Impact for its field and year | 4 / 6 | Cited 14.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 3 / 4 | Cited 59 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Jeffrey L. Cummings: h-index 175 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Cornell University: institution h-index 1,649 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 17 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 38382465) |
| Free to read | 2 / 2 | Free to read |
| Total | 50 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.xcrm.2023.101379
- PubMed: 38382465
- Original source →
- Free full text: Cell Reports Medicine →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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