The DOI matches its Crossref record; 57 of the 58 references it cites were found in a registry or an index.
De Novo Design of New Chemical Entities for SARS-CoV-2 Using Artificial Intelligence
Open accessPublished version of a preprint
Abstract
Background: The novel coronavirus SARS-CoV-2 has severely affected the health and economy of several countries. Multiple studies are in progress to design novel therapeutics against the potential target proteins in SARS-CoV-2, including 3CL protease, an essential protein for virus replication. Materials & methods: In this study we employed deep neural network-based generative and predictive models for de novo design of small molecules capable of inhibiting the 3CL protease. The generative model was optimized using transfer learning and reinforcement learning to focus around the chemical space corresponding to the protease inhibitors. Multiple physicochemical property filters and virtual screening score were used for the final screening. Conclusion: We have identified 33 potential compounds as ideal candidates for further synthesis and testing against SARS-CoV-2.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In some cases, these AI-powered approaches have been able to identify promising drug candidates in a fraction of the time that it would take when using traditional methods [26,27,28,29,30,31].”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [30]
In the citing paper
“ML algorithms have been used to analyze large datasets of potential compounds and identify those with the most potential for treating the virus.”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [30]
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412
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
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Home visits: a strategy to improve newborn survival - Authors' reply
(2008) · Americanae (AECID Library) · doi:10.1016/s0140 · Cited by 22,524
A pneumonia outbreak associated with a new coronavirus of probable bat origin
Zhou, P., Yang, X.-L., Wang, X.-G., Hu, B., Zhang, L., Zhang, W., Si, H.-R., Zhu, Y., Li, B., Huang, C.-L., et al. (2020) · Nature · doi:10.1038/s41586-020-2012-7 · Cited by 21,969
Hoffmann, M., Kleine-Weber, H., Schroeder, S., Krüger, N., Herrler, T., Erichsen, S., Schiergens, T. S., Herrler, G., Wu, N.-H., Nitsche, A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.02.052 · Cited by 20,286
Daina, A., Michielin, O., & Zoete, V. (2017) · Scientific Reports · doi:10.1038/srep42717 · Cited by 18,367
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008
Pires, D. E. V., Blundell, T. L., & Ascher, D. B. (2015) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b00104 · Cited by 6,126
Isolation of a Novel Coronavirus from a Man with Pneumonia in Saudi Arabia
Zaki, A. M., van Boheemen, S., Bestebroer, T. M., Osterhaus, A. D. M. E., & Fouchier, R. A. M. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1211721 · Cited by 5,677
A Trial of Lopinavir–Ritonavir in Adults Hospitalized with Severe Covid-19
Cao, B., Wang, Y., Wen, D., Liu, W., Wang, J., Fan, G., Ruan, L., Song, B., Cai, Y., Wei, M., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001282 · Cited by 5,258
Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors
Jin, Z., Du, X., Xu, Y., Deng, Y., Liu, M., Zhao, Y., Zhang, B., Li, X., Zhang, L., Peng, C., et al. (2020) · Nature · doi:10.1038/s41586-020-2223-y · Cited by 4,482
ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr777 · Cited by 4,382
Baell, J. B., & Holloway, G. A. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm901137j · Cited by 4,196
A Survey on Deep Transfer Learning
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01424-7_27 · Cited by 3,165
Letko, M., Marzi, A., & Munster, V. (2020) · Nature Microbiology · doi:10.1038/s41564-020-0688-y · Cited by 3,109
A Deep Learning Approach to Antibiotic Discovery
Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.021 · Cited by 2,463
Wu, C., Liu, Y., Yang, Y., Zhang, P., Zhong, W., Wang, Y., Wang, Q., Xu, Y., Li, M., Li, X., et al. (2020) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2020.02.008 · Cited by 2,367
Ertl, P., & Schuffenhauer, A. (2009) · Journal of Cheminformatics · doi:10.1186/1758-2946-1-8 · Cited by 1,939
Deep learning enables rapid identification of potent DDR1 kinase inhibitors
Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573
Structure-based design of antiviral drug candidates targeting the SARS-CoV-2 main protease
Dai, W., Zhang, B., Jiang, X.-M., Su, H., Li, J., Zhao, Y., Xie, X., Jin, Z., Peng, J., Liu, F., et al. (2020) · Science · doi:10.1126/science.abb4489 · Cited by 1,517
Molecular de-novo design through deep reinforcement learning
Olivecrona, M., Blaschke, T., Engkvist, O., & Chen, H. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0235-x · Cited by 1,443
Deep reinforcement learning for de novo drug design
Popova, M., Isayev, O., & Tropsha, A. (2018) · Science Advances · doi:10.1126/sciadv.aap7885 · Cited by 1,433
SMILES. 2. Algorithm for generation of unique SMILES notation
Weininger, D., Weininger, A., & Weininger, J. L. (1989) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00062a008 · Cited by 1,326
GuacaMol: Benchmarking Models for de Novo Molecular Design
Brown, N., Fiscato, M., Segler, M. H. S., & Vaucher, A. C. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00839 · Cited by 895
Pillaiyar, T., Manickam, M., Namasivayam, V., Hayashi, Y., & Jung, S. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b01461 · Cited by 773
Lessons Learnt from Assembling Screening Libraries for Drug Discovery for Neglected Diseases
Brenk, R., Schipani, A., James, D., Krasowski, A., Gilbert, I. H., Frearson, J. A., & Wyatt, P. G. (2007) · ChemMedChem · doi:10.1002/cmdc.200700139 · Cited by 772
Organic Carbamates in Drug Design and Medicinal Chemistry
Ghosh, A. K., & Brindisi, M. (2015) · Journal of Medicinal Chemistry · doi:10.1021/jm501371s · Cited by 752
SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping
Wu, Y., Zhang, F., Yang, K., Fang, S., Bu, D., Li, H., Sun, L. D., Hu, H., Gao, K., Wang, W., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1021 · Cited by 572
An evaluation of the implementation of the Cramer classification scheme in the Toxtree software
Patlewicz, G. Y., Jeliazkova, N., Safford, R. J., Worth, A. P., & Aleksiev, B. (2008) · SAR and QSAR in environmental research · doi:10.1080/10629360802083871 · Cited by 515
Coronavirus puts drug repurposing on the fast track
Harrison, C. (2020) · Nature Biotechnology · doi:10.1038/d41587-020-00003-1 · Cited by 474
Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery
Preuer, K., Renz, P., Unterthiner, T., Hochreiter, S., & Klambauer, G. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00234 · Cited by 416
From SARS to MERS: 10 years of research on highly pathogenic human coronaviruses
Hilgenfeld, R., & Peiris, M. J. S. (2013) · Antiviral Research · doi:10.1016/j.antiviral.2013.08.015 · Cited by 359
(1992) · Springer eBooks · doi:10.1007/978-1-4615-3618-5 · Cited by 334
Fan, K., Wei, P., Feng, Q., Chen, S., Huang, C., Ma, L., Lai, B., Pei, J., Liu, Y., Chen, J., et al. (2004) · Journal of Biological Chemistry · doi:10.1074/jbc.m310875200 · Cited by 329
SARS-CoV 3CL protease cleaves its C-terminal autoprocessing site by novel subsite cooperativity
Muramatsu, T., Takemoto, C., Kim, Y.-T., Wang, H., Nishii, W., Terada, T., Shirouzu, M., & Yokoyama, S. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1601327113 · Cited by 268
A proof of the triangle inequality for the Tanimoto distance
Lipkus, A. H. (1999) · Journal of Mathematical Chemistry · doi:10.1023/a:1019154432472 · Cited by 232
Inferring Algorithmic Patterns with Stack-Augmented Recurrent NetsPreprint
Joulin, A., & Mikolov, T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.01007 · Cited by 228
ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?Preprint
Benhenda, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.08227 · Cited by 133
Potential 2019-nCoV 3C-like Protease Inhibitors Designed Using Generative Deep Learning ApproachesPreprint
Zhavoronkov, A., Aladinskiy, V., Zhebrak, A., Zagribelnyy, B. A., Terentiev, V. A., Bezrukov, D., Polykovskiy, D. A., Shayakhmetov, R., Filimonov, A., Orekhov, P., et al. (2020) · ChemRxiv · doi:10.26434/chemrxiv.11829102.v1 · Cited by 108
Kleandrova, V. V., Ruso, J. M., Speck‐Planche, A., & Cordeiro, M. N. D. S. (2016) · ACS Combinatorial Science · doi:10.1021/acscombsci.6b00063 · Cited by 92
On the Convergence Proof of AMSGrad and a New Version
Phuong, T. T., & Phong, L. T. (2019) · IEEE Access · doi:10.1109/access.2019.2916341 · Cited by 91
Zhou, B., Yang, Z., Feng, Q., Liang, X., Li, J., Zanin, M., Jiang, Z., & Zhong, N. (2017) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2017.01.038 · Cited by 80
A unified lead-oriented synthesis of over fifty molecular scaffolds
Doveston, R. G., Tosatti, P., Dow, M., Foley, D. J., Li, H. Y., Campbell, A. J., House, D., Churcher, I., Marsden, S. P., & Nelson, A. S. (2014) · Organic & Biomolecular Chemistry · doi:10.1039/c4ob02287d · Cited by 74
Speck‐Planche, A., & Cordeiro, M. N. D. S. (2017) · ACS Combinatorial Science · doi:10.1021/acscombsci.7b00039 · Cited by 69
Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning ApproachesPreprint
Zhavoronkov, A. A., Aladinskiy, V., Zhebrak, A., Zagribelnyy, B. A., Terentiev, V. A., Bezrukov, D. S., Polykovskiy, D. A., Shayakhmetov, R., Filimonov, A., Orekhov, P. S., et al. (2020) · ChemRxiv · doi:10.26434/chemrxiv.11829102.v2 · Cited by 63
Aurantiamides: A new class of modified dipeptides from Piper aurantiacum
Banerji, A., & Ray, R. (1981) · Phytochemistry · doi:10.1016/0031-9422(81)80116-1 · Cited by 58
Speck‐Planche, A., & Cordeiro, M. N. D. S. (2017) · Medicinal Chemistry Research · doi:10.1007/s00044-017-1936-4 · Cited by 58
Tuning Recurrent Neural Networks with Reinforcement LearningPreprint
Jaques, N., Gu, S., Turner, R. E., & Douglas Eck, C. (2016) · arXiv (Cornell University) · Cited by 54
Suwal, S., & Kodadek, T. J. (2013) · Organic & Biomolecular Chemistry · doi:10.1039/c3ob27476d · Cited by 32
PaccMannRL: Designing Anticancer Drugs From Transcriptomic Data via Reinforcement Learning
Born, J., Manica, M., Oskooei, A., Cadow, J., & Martínez, M. R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45257-5_18 · Cited by 32
Advance of promising targets and agents against COVID-19 in China
Duan, Y., Zhu, H., & Zhou, C. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.02.011 · Cited by 29
Lin, W., Huang, W., Ning, S., Wang, X., Qi, Y., & Wei, D. (2018) · PLoS ONE · doi:10.1371/journal.pone.0199788 · Cited by 21
Speck‐Planche, A. (2018) · Future Medicinal Chemistry · doi:10.4155/fmc-2018-0213 · Cited by 21
Flagged first, then by citations
1 of the 58 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
The role of AI in drug discovery: challenges, opportunities, and strategies
Blanco-González, A., Cabezón, A., Seco-González, A., Conde-Torres, D., Antelo-Riveiro, P., Piñeiro, Á., & Garcia-Fandino, R. (2023) · Pharmaceuticals · doi:10.3390/ph16060891 · Cited by 690
Mapping the landscape of Artificial Intelligence applications against COVID-19
Aylett-Bullock, J., Luccioni, A. S., Pham, K. H., Lam, C. S. N., & Luengo-Oroz, M. A. (2020) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12162 · Cited by 533
Leveraging Data Science to Combat COVID-19: A Comprehensive Review
Latif, S., Usman, M., Manzoor, S., Iqbal, W., Qadir, J., Gareth Tyson, A., Castro, I., Razi, A., Boulos, M. N. K., Weller, A., et al. (2020) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2020.3020521 · Cited by 273
Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development
Arshadi, A. K., Webb, J., Salem, M., Cruz, E., Calad-Thomson, S., Ghadirian, N., Collins, J., Diez‐Cecilia, E., Kelly, B., Goodarzi, H., et al. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00065 · Cited by 253
Natural product drug discovery in the artificial intelligence era
Saldívar‐González, F. I., Aldas-Bulos, V. D., Medina‐Franco, J. L., & Plisson, F. (2021) · Chemical Science · doi:10.1039/d1sc04471k · Cited by 235
Metadata
- Authors
- Bung, N., Krishnan, S. R., Bulusu, G., & Roy, A.
- Affiliations
- Tata Consultancy Services (India) (IN)
- Corresponding author
- Bulusu, Gopalakrishnan
- Roy, Arijit
- Venue
- Future Medicinal Chemistry
- Publisher
- Informa UK Limited
- Published
- 2021-02-16
- Volume
- 13
- Issue
- 6
- Pages
- 575-585
- ISSN
- 1756-8919 (ISSN-L)
- 1756-8927
- PMC
- PMC7888348
- OpenAlex
- W3090171308
- Cited by
- 118
- References
- 58
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 17 · 2023: 13 · 2022: 24 · 2021: 29
- FWCI
- 10.81
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Future Medicinal Chemistry (publishedVersion)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Royal College of Surgeons in Ireland (submittedVersion, cc-by-nc-nd)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Keywords
- SARS-CoV-2, main protease, Mpro inhibitors, de novo drug design, generative models, deep neural network, transfer learning, reinforcement learning, virtual screening, antiviral agents
- MeSH terms
- COVID-19, SARS-CoV-2, Coronavirus 3C Proteases, COVID-19 Drug Treatment, Antiviral Agents, Artificial Intelligence, Humans, Ligands, Protease Inhibitors, Drug Design, Drug Discovery, Molecular Docking Simulation
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
