The transformational role of GPU computing and deep learning in drug discovery
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Deep learning workflows in environmental applications usually incorporate large spatiotemporal datasets, repeated model training, and computationally demanding mapping procedures, which makes acceleration important in practice.
“Deep learning workflows in environmental applications usually incorporate large spatiotemporal datasets, repeated model training, and computationally demanding mapping procedures, which makes acceleration important in practice [19].”
From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [19]
GPU acceleration plays an important practical role, enabling faster training and inference in modern deep learning workflows.
“Therefore, GPU acceleration plays an important practical role, enabling faster training and inference in modern deep learning workflows [19, 20].”
From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [19]
Cites
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · see citing works
QSAR Modeling: Where have you been? Where are you going to?
Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., Dearden, J., Gramatica, P., Martin, Y. C., Todeschini, R., et al. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm4004285 · Cited by 2,164 · see citing works
Self-citationA Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3130191 · Cited by 1,231 · see citing works
DeepTox: toxicity prediction using deep learning
Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00080 · Cited by 1,081 · see citing works
A graph-convolutional neural network model for the prediction of chemical reactivity
Coley, C. W., Jin, W., Rogers, L., Jamison, T. F., Jaakkola, T. S., Green, W. H., Barzilay, R., & Jensen, K. F. (2019) · Chemical Science · doi:10.1039/c8sc04228d · Cited by 679 · see citing works
Exploiting machine learning for end-to-end drug discovery and development
Ekins, S., Puhl, A. C., Zorn, K. M., Lane, T. R., Russo, D. P., Klein, J. J., Hickey, A. J., & Clark, A. M. (2019) · Nature Materials · doi:10.1038/s41563-019-0338-z · Cited by 578 · see citing works
Graph Convolutional Neural Networks for Predicting Drug-Target Interactions
Torng, W., & Altman, R. B. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00628 · Cited by 371 · see citing works
Utilizing graph machine learning within drug discovery and development
Gaudelet, T., Day, B., Jamasb, A. R., Soman, J., Regep, C., Liu, G., Hayter, J. B. R., Vickers, R., Roberts, C., Tang, J., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab159 · Cited by 313 · see citing works
TorchMD: A Deep Learning Framework for Molecular Simulations
Doerr, S., Majewski, M., Pérez, A., Krämer, A., Clementi, C., Noe, F., Giorgino, T., & De Fabritiis, G. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c01343 · Cited by 230 · see citing works
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Nordquist, E. B., Zhao, M., Kumar, A., & MacKerell, A. D. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01189 · Cited by 15 · see citing works
GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment
Alihodžić, A. (2026) · arXiv · doi:10.48550/arxiv.2605.16435
“Therefore, GPU acceleration plays an important practical role, enabling faster training and inference in modern deep learning workflows [19, 20].”
- Authors
- Pandey, M., Fernandez, M., Gentile, F., Isayev, O., Tropsha, A., Stern, A. C., & Cherkasov, A.
- Affiliations
- University of British Columbia (CA)
- Carnegie Mellon University (US)
- University of North Carolina at Chapel Hill (US)
- Nvidia (United States) (US)
- Corresponding author
- Stern, Abraham C.
- Cherkasov, Artem
- ORCID
- Venue
- Nature Machine Intelligence
- Published
- 2022-03-23
- Volume
- 4
- Issue
- 3
- Pages
- 211-221
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W4291288469
- Cited by
- 255
- References
- 9
- Citations by year
- 2026: 34 · 2025: 74 · 2024: 79 · 2023: 55 · 2022: 12 · 2021: 1
- FWCI
- 30.44
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- Michael Smith Health Research BC
- Vancouver Coastal Health Research Institute
- Canadian Institutes of Health Research (170631_1, 2019 Novel Coronavirus (2019-nCoV) Rapid Research, 170631)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Keywords
- GPU computing, deep learning, drug discovery, GPU acceleration, molecular docking, off-target effects, hit-to-lead optimization, drug target identification, parallel computing
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