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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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1 retracted
Ranković, Z. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b01469 · Cited by 97
RetractedVaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
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
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513
Research works printed but not linked: 8
- Goebel, R. et al. Explainable AI: the new 42? In Machine Learning and Knowledge Extraction. CD-MAKE 2018. Lecture Notes in Computer Science Vol. 11015 (eds Holzinger, A., Kieseberg, P., Tjoa, A. & Weippl, E) (Springer, 2018).
- Chander, A., Srinivasan, R., Chelian, S., Wang, J. & Uchino, K. Working with beliefs: AI transparency in the enterprise. In Joint Proceedings of the ACM IUI 2018 Workshops co-located with the 23rd ACM Conference on Intelligent User Interfaces 2068 (eds Said, A. & Komatsu, T.) (CEUR-WS.org, 2018).
- Wachter, S., Mittelstadt, B. & Russell, C. Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harv. J. Law Technol. 31, 841–888 (2017).
- Baldassarre, F. & Azizpour, H. Explainability techniques for graph convolutional networks. In International Conference on Machine Learning (ICML) Workshops, 2019 Workshop on Learning and Reasoning with Graph-Structured Representations (ICML, 2019).
- Leake, D. B. in Case-based Reasoning: Experiences, Lessons and Future Directions, ch. 2 (ed. Leake, D. B.) (MIT Press, 1996).
- Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K. & Wierstra, D. Matching networks for one shot learning. Adv. Neural Inf. Process. Syst. 29, 3630–3638 (2016).
- Lakshminarayanan, B., Pritzel, A. & Blundell, C. Simple and scalable predictive uncertainty estimation using deep ensembles. Adv. Neural Inf. Process. Syst. 30, 6402–6413 (2017).
- Huang, G. et al. Snapshot ensembles: train one, get m for free. Preprint at https://arxiv.org/abs/1704.00109 (2017). · arXiv
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
Siala, H., & Wang, Y. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.114782 · Cited by 358
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 .”
Nasarian, E., Alizadehsani, R., Acharya, U. R., & Tsui, K.-L. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102412 · Cited by 207
Explainable artificial intelligence in Alzheimer’s disease classification: a systematic review
Viswan, V., Shaffi, N., Mahmud, M., Subramanian, K., & Hajamohideen, F. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10192-x · Cited by 195
“XAI has been applied in drug discovery [106, 107], industrial applications [108, 109], gaming [110, 111], neurological disorders [112–114], neuroscience [115, 116] and recommender systems [117, 118].”
Bai, Q., Liu, S., Tian, Y., Xu, T., Banegas‐Luna, A. J., Pérez‐Sánchez, H., Huang, J., Liu, H., & Yao, X. (2022) · WIREs Computational Molecular Science · doi:10.1002/wcms.1581 · Cited by 156
Model agnostic generation of counterfactual explanations for molecules
Wellawatte, G. P., Seshadri, A., & White, A. D. (2022) · Chemical Science · doi:10.1039/d1sc05259d · Cited by 143
EXplainable Artificial Intelligence (XAI)—From Theory to Methods and Applications
Ortigossa, E. S., Gonçalves, T., & Nonato, L. G. (2024) · IEEE Access · doi:10.1109/access.2024.3409843 · Cited by 124
Explainable artificial intelligence for drug discovery and development: a comprehensive survey
Alizadehsani, R., Oyelere, S. S., Hussain, S., Jagatheesaperumal, S. K., Calixto, R. R., Rahouti, M., Roshanzamir, M., & De Albuquerque, V. H. C. (2024) · IEEE Access · doi:10.1109/access.2024.3373195 · Cited by 124
Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment
Jiménez-Luna, J., Skalic, M., Weskamp, N., & Schneider, G. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01344 · Cited by 96
Self-citationQuantitative evaluation of explainable graph neural networks for molecular property prediction
Rao, J., Zheng, S., Lu, Y., & Yang, Y. (2022) · Patterns · doi:10.1016/j.patter.2022.100628 · Cited by 90
Extending the Nested Model for User-Centric XAI: A Design Study on GNN-based Drug Repurposing
Wang, Q., Huang, K., Chandak, P., Zitnik, M., & Gehlenborg, N. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3209435 · Cited by 82
Artificial Intelligence for Biology
Hassoun, S., Jefferson, F., Shi, X., Stucky, B., Wang, J., & Rosa, E. (2022) · Integrative and Comparative Biology · doi:10.1093/icb/icab188 · Cited by 64
Deep learning for low-data drug discovery: Hurdles and opportunities
van Tilborg, D., Brinkmann, H., Criscuolo, E., Rossen, L., Özçelik, R., & Grisoni, F. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102818 · Cited by 61
Self-citationNiazi, S. K. (2025) · Pharmaceuticals · doi:10.3390/ph18091271 · Cited by 61
Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
Duran-Frigola, M., Cigler, M., & Winter, G. E. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11098 · Cited by 50
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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