The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery
Open accessAbstract
ABSTRACT Drug discovery is an extensive and rigorous process that requires up to 2 billion dollars of investments and more than ten years of research and development to bring a molecule “from bench to a bedside”. While virtual screening can significantly enhance drug discovery workflow, it ultimately lags the current rate of expansion of chemical databases that already incorporate billions of purchasable compounds. This surge of available small molecules presents great opportunities for drug discovery but also demands for faster virtual screening methods and protocols. In order to address this challenge, we herein introduce Deep Docking ( D 2 ) - a novel deep learning-based approach which is suited for docking billions of molecular structures. The developed D 2 -platform utilizes quantitative structure-activity relationship (QSAR) based deep models trained on docking scores of subsets of a large chemical library (Big Base) to approximate the docking outcome for yet unprocessed molecular entries and to remove unfavorable structures in an iterative manner. We applied D 2 to virtually screen 1.36 billion molecules form the ZINC15 library against 12 prominent target proteins, and demonstrated up to 100-fold chemical data reduction and 6,000-fold enrichment for top hits, without notable loss of well-docked entities. The developed D 2 protocol can readily be used in conjunction with any docking program and was made publicly available.
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Cites
Lead- and drug-like compounds: the rule-of-five revolution
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ZINC − A Free Database of Commercially Available Compounds for Virtual Screening
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Innovation in the pharmaceutical industry: New estimates of R&D costs
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Molecular basis of agonism and antagonism in the oestrogen receptor
Brzozowski, A. M., Pike, A. C. W., Dauter, Z., Hubbard, R. E., Bonn, T., Engström, O., Öhman, L., Greene, G. L., Gustafsson, J.-Å., & Carlquist, M. (1997) · Nature · doi:10.1038/39645 · Cited by 3,355
ZINC 15 – Ligand Discovery for Everyone
Sterling, T., & Irwin, J. J. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00559 · Cited by 3,224
Principles of early drug discovery
Hughes, J., Rees, S., Kalindjian, S., & Philpott, K. (2011) · British Journal of Pharmacology · doi:10.1111/j.1476-5381.2010.01127.x · Cited by 2,859
A comprehensive map of molecular drug targets
Santos, R., Ursu, O., Gaulton, A., Bento, A. P., Donadi, R. S., Bologa, C. G., Karlsson, A., Al-Lazikani, B., Hersey, A., Oprea, T. I., et al. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.230 · Cited by 2,427
Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking.
Mysinger, M. M., Carchia, M., Irwin, J. J., & Shoichet, B. K. (2012) · Journal of Medicinal Chemistry · doi:10.1021/jm300687e · Cited by 2,412
The rise of deep learning in drug discovery
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Cited by
Ton, A., Gentile, F., Hsing, M., Ban, F., & Cherkasov, A. (2020) · Molecular Informatics · doi:10.1002/minf.202000028 · Cited by 517
Self-citationGentile, F., Yaacoub, J. C., Gleave, J., Fernandez, M., Ton, A.-T., Ban, F., Stern, A., & Cherkasov, A. (2022) · Nature Protocols · doi:10.1038/s41596-021-00659-2 · Cited by 397
Self-citationExposing the Limitations of Molecular Machine Learning with Activity Cliffs
van Tilborg, D., Alenicheva, A., & Grisoni, F. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01073 · Cited by 337
Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery
Wong, F., Krishnan, A., Zheng, E. J., Stärk, H., Manson, A. L., Earl, A. M., Jaakkola, T., & Collins, J. J. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211081 · Cited by 284
“Model predictions based on the following rescoring functions were ensembled in sequence: RF-Score, NNScore, PLEC score, and RF-Score-VS.”
BackgroundMethodAn artificial intelligence accelerated virtual screening platform for drug discovery
(2024) · doi:10.1038/s41467-024-52061-7 · Cited by 259
Metadata
- Authors
- Gentile, F., Agrawal, V., Hsing, M., Ton, A.-T., Ban, F., Norinder, U., Gleave, M. E., & Cherkasov, A.
- Venue
- ACS Central Science
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-05-19
- Volume
- 6
- Issue
- 6
- Pages
- 939-949
- ISSN
- 2374-7943 (ISSN-L)
- 2374-7951
- OpenAlex
- W3027975282
- Cited by
- 447
- References
- 40
- FWCI
- 24.89
- Type
- journal article
- Language
- en
- Open access
- diamond
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