The DOI matches its Crossref record; 256 of the 256 references it cites were found in a registry or an index.
Artificial intelligence in cancer target identification and drug discovery
Open accessAbstract
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1131 · Cited by 17,749
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Research works printed but not linked: 23
- Liu, G. D., Li, Y. C., Zhang, W. & Zhang, L. A brief review of artificial intelligence applications and algorithms for psychiatric disorders. Eng.-Prc 6, 462–467 (2020).
- Lanza, V. F., Baquero, F., Cruz, F. D. L. & Coque, T. M. AccNET (Accessory Genome Constellation Network): comparative genomics software for accessory genome analysis using bipartite networks. Bioinformatics 33, btw601 (2016).
- Zhang, L. et al. Investigation of mechanism of bone regeneration in a porous biodegradable calcium phosphate (CaP) scaffold by a combination of a multi-scale agent-based model and experimental optimization/validation. Nanoscale 8, 14877–14887 (2016). · doi:10.1039/c6nr01637e
- Loh, W.-Y. Classification and regression trees. Phys. Rep. 1, 14–23 (2011).
- Nordhausen & Klaus An introduction to statistical learning—with applications in R by Gareth James, Daniela Witten, Trevor Hastie & Robert Tibshirani. Int. Stat. Rev. 82, 156–157 (2014). · doi:10.1111/insr.12051_19
- Rabbani, M. & Kazemi, S. Solving uncapacitated multiple allocation p-hub center problem by Dijkstra’s algorithm-based genetic algorithm and simulated annealing. Int. J. Ind. Eng. Comput. 6, 405–418 (2015).
- Jalili, M. et al. Evolution of centrality measurements for the detection of essential proteins in biological networks. Front. Physiol. 7, 375 (2016). · DOI
- Pastor-Satorras, R. & Castellano, C. Distinct types of eigenvector localization in networks. Sci. Rep. 6, 18847 (2016). · DOI
- Li, H. et al. Deciphering the mechanism of Indirubin and its derivatives in the inhibition of Imatinib resistance using a “drug target prediction-gene microarray analysis-protein network construction” strategy. BMC Complement. Alter. Med. 19, 75 (2019). · doi:10.1186/s12906-019-2471-2
- Al-Aamri, A., Taha, K., Al-Hammadi, Y., Maalouf, M. & Homouz, D. Analyzing a co-occurrence gene-interaction network to identify disease-gene association. BMC Bioinform. 20, 70 (2019). · doi:10.1186/s12859-019-2634-7
- Ye, N., Zhang, Y., Wang, R. & Malekian, R. Vehicle trajectory prediction based on Hidden Markov Model. KSII Trans. Internet Infor. Syst. 10, 3150–3170 (2016).
- Freund, Y. & Mason, L. The Alternating Decision Tree Learning Algorithm. In Proc. Sixteenth International Conference on Machine Learning, 124–133 (1999).
- Vaswani, A. et al. Attention is all you need. In Proc. 31st International Conference on Neural Information Processing Systems, 5998–6008 (2017).
- Wang, A. et al. Cell adhesion-related molecules play a key role in renal cancer progression by multinetwork analysis. Biomed. Res. Int. 2019, 2325765 (2019). · doi:10.1155/2019/2325765
- Gigliotti, B. J., Russell, M. D., Shonka, D. & Stathatos, N. Fine-needle aspiration and molecular analysis. Surgery of the Thyroid and Parathyroid Glands (Third Edition), 118–131, https://doi.org/10.1016/B978-0-323-66127-0.00012-0 (2021). · doi:10.1016/b978-0-323-66127-0.00012-0
- Vermeulen, M. & Lelie, N. The current status of nucleic acid amplification technology in transfusion-transmitted infectious disease testing. ISBT Sci. Ser. 11, 123–128 (2016). · doi:10.1111/voxs.12256
- Roda, A., Guardigli, M., Pasini, P. & Mirasoli, M. Bioluminescence and chemiluminescence in drug screening. Anal. Bioanal. Chem. 377, 826–833 (2003). · doi:10.1007/s00216-003-2096-6
- Oliver, S. Guilt-by-association goes global. Nature 403, 601–602 (2000). · DOI · Paper page
- Luo, H. et al. Drug repositioning based on comprehensive similarity measures and Bi-Random Walk algorithm. Bioinformatics 32, btw228 (2016). · DOI · Paper page
- Honda, S., Shi, S. & Ueda, H. R. SMILES transformer: pre-trained molecular fingerprint for low data drug discovery. CoRR abs/1911.04738 (2019). · arXiv
- Zhong, H. A. ADMET properties: overview and current topics. Drug Design: Principles and Applications, 113–133, https://doi.org/10.1007/978-981-10-5187-6_8 (2017). · doi:10.1007/978-981-10-5187-6_8
- Yauney, G. & Shah, P. Reinforcement learning with action-derived rewards for chemotherapy and clinical trial dosing regimen selection. Proc. 3rd Mach. Learn. Healthc. Conf. 85, 161–226 (2018).
- Hiba Abi, H. PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins. Nucleic Acids Res. 43, W436–442 (2015). · DOI · Paper page
Cited by
Xu, Z., Ren, F., Wang, P., Cao, J., Tan, C., Ma, D., Zhao, L., Dai, J., Ding, Y., Fang, H., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03743-2 · Cited by 180
“…have demonstrated proof-of-concept innovation and potential to accelerate and decrease costs across all stages of pharmaceutical research and development by facilitating disease-associated target prioritization 3–5 , compound design and optimization 6–10 , and clinical trial performance 11–15 .”
Self-citationSingh, S., Kumar, R., Payra, S., & Singh, S. K. (2023) · Cureus · doi:10.7759/cureus.44359 · Cited by 154
Artificial intelligence for drug discovery and development in Alzheimer's disease
Qiu, Y., & Cheng, F. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102776 · Cited by 83
Leivaditis, V., Maniatopoulos, A. A., Lausberg, H., Mulita, F., Papatriantafyllou, A., Liolis, E., Beltsios, E., Adamou, A., Kontodimopoulos, N., & Dahm, M. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14082729 · Cited by 76
Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75
Metadata
- Authors
- You, Y., Lai, X., Pan, Y., Zheng, H., Vera, J., Liu, S., Deng, S., & Zhang, L.
- Venue
- Signal Transduction and Targeted Therapy
- Published
- 2022-05-10
- Volume
- 7
- Issue
- 1
- Pages
- 156-156
- ISSN
- 2059-3635 (ISSN-L)
- OpenAlex
- W4229447215
- Cited by
- 435
- References
- 256
- FWCI
- 44.34
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Artificial intelligence in cancer target identification and drug discovery. https://app.cochat.ai/papers/10.1038/s41392-022-00994-0
- The paper
- You, Y., Lai, X., Pan, Y., Zheng, H., Vera, J., Liu, S., Deng, S., & Zhang, L. (2022). Artificial intelligence in cancer target identification and drug discovery. Signal Transduction and Targeted Therapy. https://doi.org/10.1038/s41392-022-00994-0
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.