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Artificial intelligence for drug discovery and development in Alzheimer's disease
Qiu Y, Cheng F.. Current opinion in structural biology, 2024-02-08
Topic: Amyloid, tau and blood-test diagnosis
The complex molecular mechanism and pathophysiology of Alzheimer's disease (AD) limits the development of effective therapeutics or prevention strategies. Artificial Intelligence (AI)-guided drug discovery combined with genetics/multi-omics (genomics, epigenomics, transcriptomics, proteomics, and metabolomics) analysis contributes to the understanding of the pathophysiology and precision medicine of the disease, including AD and AD-related dementia. In this review, we summarize the AI-driven methodologies for AD-agnostic drug discovery and development, including de novo drug design, virtual screening, and prediction of drug-target interactions, all of which have shown potentials. In particular, AI-based drug repurposing emerges as a compelling strategy to identify new indications for existing drugs for AD. We provide several emerging AD targets from human genetics and multi-omics findings and highlight recent AI-based technologies and their applications in drug discovery using AD as a prototypical example. In closing, we discuss future challenges and directions in AI-based drug discovery for AD and other neurodegenerative diseases.
Key findings
- Artificial Intelligence (AI)-guided drug discovery combined with genetics/multi-omics (genomics, epigenomics, transcriptomics, proteomics, and metabolomics) analysis contributes to the understanding of the pathophysiology and precision medicine of the disease, including AD and AD-related dementia.
- In this review, we summarize the AI-driven methodologies for AD-agnostic drug discovery and development, including de novo drug design, virtual screening, and prediction of drug-target interactions, all of which have shown potentials.
- In particular, AI-based drug repurposing emerges as a compelling strategy to identify new indications for existing drugs for AD.
- We provide several emerging AD targets from human genetics and multi-omics findings and highlight recent AI-based technologies and their applications in drug discovery using AD as a prototypical example.
- In closing, we discuss future challenges and directions in AI-based drug discovery for AD and other neurodegenerative diseases.
Why it matters
This review describes how artificial intelligence, combined with genetics and multi-omics data, is being used to search for Alzheimer's drugs, including repurposing existing drugs. It is a summary of methods and potential, not evidence that any treatment works.
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Study details
- Evidence strengthLow
How this study scored 47/100
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 (Current opinion in structural biology) |
| Journal's citation record | 3 / 4 | Current Opinion in Structural Biology: journal h-index 252 (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 81 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 20.2 citations a year over 4 year(s) since 2024 |
| Impact for its field and year | 2 / 6 | Cited 3.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 3 other studies in this library |
| Still being cited | 3 / 4 | Cited 80 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Feixiong Cheng: h-index 94 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Case Western Reserve University: institution h-index 1,159 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 38335558) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 47 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.sbi.2024.102776
- PubMed: 38335558
- Original source →
- Free full text: Current Opinion in Structural Biology →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Qiu, Y., & Cheng, F. (2024). Artificial intelligence for drug discovery and development in Alzheimer's disease. Current opinion in structural biology. https://doi.org/10.1016/j.sbi.2024.102776
Vancouver
Qiu Y, Cheng F. Artificial intelligence for drug discovery and development in Alzheimer's disease. Current opinion in structural biology. 2024. doi:10.1016/j.sbi.2024.102776. PMID: 38335558.
BibTeX
@article{qiu2024-38335558,
author = {Qiu, Y. and Cheng, F.},
title = {{Artificial intelligence for drug discovery and development in Alzheimer's disease}},
journal = {Current opinion in structural biology},
year = {2024},
doi = {10.1016/j.sbi.2024.102776},
pmid = {38335558},
url = {https://doi.org/10.1016/j.sbi.2024.102776}
}
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