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.
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“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]
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“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]
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Potential Drug Candidates for SARS-CoV-2 Using Computational Screening and Enhanced Sampling MethodsPreprint
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TRACEDD: A Tool-grounded Reasoning and Agentic Coordination for Explainable Drug DesignPreprint
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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
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