Paper · 33239267
Artificial Intelligence Predicts Drug Response.
Cancer Discov, 2021. Cited by 4 papers, by iCite.
11 sourcespapers-2026-11-12-0.1.0
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ConductScience Library. Artificial Intelligence Predicts Drug Response. Papers, record 33239267. Release papers-2026-11-12-0.1.0, 12 Nov 2026. https://conductscience.org/library/papers/33239267
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Paper
PubMed's record of the paper, with iCite's citation figures. No abstract is shown.
Topics5
NLM's MeSH indexing of the paper, its major topics first.
| MeSH term | Major |
|---|---|
| Deep Learning | Major topic |
| Neoplasms | Major topic |
| Pharmaceutical Preparations | Major topic |
| Artificial Intelligence | |
| Humans |
Source PubMed
Grants0
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None in this release.
Trials0
The trials whose ClinicalTrials.gov record lists the paper, and the trial numbers the paper's PubMed record names.
None in this release.
Comments and corrections1
PubMed's comments and corrections, NLM's own indexing: retractions first, then expressions of concern, errata and updates, and comments last.
| Kind | PMID | Title | In Papers |
|---|---|---|---|
| Comment on | 33,096,023 | Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells. | https://conductscience.org/library/papers/33096023 |
Source PubMed
References0
The papers the reference list in PubMed's record names, those in our copy of PubMed, in the list's order.
None in this release.
Cited by3
The papers in our copy of PubMed whose reference list names this one, newest first. iCite, which reads more sources, counts 4 citations.
| PMID | Title | Journal | Year | In Papers | Source |
|---|---|---|---|---|---|
| 41,963,934 | Application of artificial intelligence in lung cancer diagnosis, therapy, and prognosis. | BMC Med Genomics | 2026 | https://conductscience.org/library/papers/41963934 | PubMed |
| 41,718,388 | Bridging the Gap Between Static Histology and Dynamic Organ-on-a-Chip Models. | Pathophysiology | 2026 | https://conductscience.org/library/papers/41718388 | PubMed |
| 38,506,918 | Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. | JMIR Med Inform | 2024 | https://conductscience.org/library/papers/38506918 | PubMed |
Genes0
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None in this release.
Drugs0
The drugs whose trials, in the Pipelines release, list the paper.
None in this release.
Data0
The data bank entries PubMed's record names. Trial numbers are under Trials.
None in this release.
Data dates
The date of each source this release read.
- Trials
- 2026-11-12
- Gene search
- 2026-11-11
- Pipelines
- 2026-09-18
Sources 11
- ChEMBL, through Open TargetsCC BY-SA 3.0
- ClinicalTrials.govNo license stated
- HGNCCC0
- iCiteCC BY 4.0
- NCBI GenePublic domain (US government)
- NIH RePORTER's grant and paper linksPublic domain
- Open TargetsCC0 1.0
- PMC's ID filePublic domain (US government)
- PubMedNLM's terms
- PubTator 3Public domain
- UniProtCC BY 4.0
Each source's terms
- ChEMBL, through Open TargetsCC BY-SA 3.0, for the parts from ChEMBL. Read on 8 Nov 2026 at https://platform-docs.opentargets.org/licence
- ClinicalTrials.govnone stated
- HGNCCC0: https://www.genenames.org/about/license/
- iCiteCC BY 4.0
- NCBI GeneNone named for the file. NCBI says information created by or for the US government on its site is in the public domain, and asks that NLM be acknowledged: https://www.ncbi.nlm.nih.gov/home/about/policies/
- NIH RePORTER's grant and paper linksPublic domain (NIH RePORTER)
- Open TargetsCC0 1.0. Open Targets asks that users cite its latest paper: Buniello et al. 2025, Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery, Nucleic Acids Research 53(D1):D1467, PMID 39657122. Read on 8 Nov 2026 at https://platform-docs.opentargets.org/licence
- PMC's ID fileNone named for the file. NCBI says information created by or for the US government on its site is in the public domain, and asks that NLM be acknowledged: https://www.ncbi.nlm.nih.gov/home/about/policies/
- PubMedNLM's terms
- PubTator 3Public domain
- UniProtCC BY 4.0. UniProt's licence file: "All databases and documents in the UniProt FTP directory may be copied and redistributed freely, without advance permission, provided that this copyright statement is reproduced with each copy." UniProt asks that users cite its latest paper: The UniProt Consortium, UniProt: the Universal Protein Knowledgebase in 2025, Nucleic Acids Research 53:D609-D617, https://doi.org/10.1093/nar/gkae1010. Read on 9 Nov 2026 at https://www.uniprot.org/help/license
ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
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