Home › Research › Drug development
Recent advances in drug repurposing using machine learning
Fabio Urbina, Ana C Puhl, Sean Ekins. Current opinion in chemical biology, 2021-07-16
Topic: Drug trials: efficacy and safety
Drug repurposing aims to find new uses for already existing and approved drugs. We now provide a brief overview of recent developments in drug repurposing using machine learning alongside other computational approaches for comparison. We also highlight several applications for cancer using kinase inhibitors, Alzheimer's disease as well as COVID-19.
Key findings
- Drug repurposing aims to find new uses for already existing and approved drugs.
- We now provide a brief overview of recent developments in drug repurposing using machine learning alongside other computational approaches for comparison.
- We also highlight several applications for cancer using kinase inhibitors, Alzheimer's disease as well as COVID-19.
New to this subject? Start with our plain-English guide: Treatments →
Study details
- Evidence strengthLow
How this study scored 41/100
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. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record, multi-centre collaboration and free to read, so those were left out: it earned 23 of the 56 points we could judge it on, which is 41 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Current opinion in chemical biology) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 3 / 5 | 2021 — within 6 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 | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 34274565) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 23 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 23 of the 56 points we could judge it on is 41 out of 100. |
| Total | 41 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Free copy unknown: no free-copy check has an answer for this record yet. That does not mean it is paywalled.
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
Urbina, F., Puhl, A. C., & Ekins, S. (2021). Recent advances in drug repurposing using machine learning. Current opinion in chemical biology. https://doi.org/10.1016/j.cbpa.2021.06.001
Vancouver
Urbina F, Puhl AC, Ekins S. Recent advances in drug repurposing using machine learning. Current opinion in chemical biology. 2021. doi:10.1016/j.cbpa.2021.06.001. PMID: 34274565.
BibTeX
@article{urbina2021-34274565,
author = {Urbina, Fabio and Puhl, Ana C and Ekins, Sean},
title = {{Recent advances in drug repurposing using machine learning}},
journal = {Current opinion in chemical biology},
year = {2021},
doi = {10.1016/j.cbpa.2021.06.001},
pmid = {34274565},
url = {https://doi.org/10.1016/j.cbpa.2021.06.001}
}