The DOI matches its Crossref record; 210 of the 210 references it cites were found in a registry or an index.
Drug discovery with explainable artificial intelligence
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“Modern algorithms such as “transformer models”, which have found tremendous success in the field of natural language processing and computer vision, have been applied to identify relevant atoms in a ligand or to pinpoint important residues in a protein binding site.274”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [274]
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Cited by
Sorted by citations
Why 90% of clinical drug development fails and how to improve it?
Sun, D., Gao, W., Hu, H., & Zhou, S. (2022) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2022.02.002 · Cited by 1,763
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473
“However, the introduction of AI has given a broader scope to develop new approaches that can improve and optimize drug discovery [442].”
BackgroundCombining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
SHIFTing artificial intelligence to be responsible in healthcare: a systematic review
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Graph representation learning in biomedicine and healthcare
Li, M. M., Huang, K., & Zitnik, M. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00942-x · Cited by 294
“Biomedically focused review articles have surveyed the use of GNNs for molecular generation 33,34 , single-cell biology 35 , drug discovery and drug repurposing 36–40 , and histopathology 41 .”
Metadata
- Authors
- Jiménez-Luna, J., Grisoni, F., & Schneider, G.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Schneider, Gisbert
- Venue
- Nature Machine Intelligence
- Published
- 2020-10-13
- Volume
- 2
- Issue
- 10
- Pages
- 573-584
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W3039995705
- Cited by
- 1,063
- References
- 210
- Citations by year
- 2026: 202 · 2025: 201 · 2024: 219 · 2023: 203 · 2022: 132 · 2021: 98 · 2020: 7
- FWCI
- 43.34
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- arXiv (Cornell University)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (205321_182176)
- Boehringer Ingelheim
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- explainable AI, drug discovery, deep learning interpretability, molecular structure prediction, molecular property prediction
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