De Novo Design of New Chemical Entities for SARS-CoV-2 Using Artificial Intelligence
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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, AI-powered approaches have identified promising drug candidates for COVID-19 in a fraction of the time that traditional methods would take.
“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]
ML algorithms have been used to analyze large datasets of potential compounds and identify those with the most potential for treating COVID-19.
“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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Towards the De Novo Design of HIV-1 Protease Inhibitors Based on Natural Products
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A Review on the Use of Machine Learning Against the Covid-19 Pandemic
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De novo design of anti-tuberculosis agents using a structure-based deep learning method
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The Artificial Intelligence-Driven Intelligent Laboratory for Organic Chemistry Synthesis
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Screening of Therapeutic Agents for COVID-19 using Machine Learning and Ensemble Docking SimulationsPreprint
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Revolution of Artificial Intelligence in Computational Chemistry Breakthroughs
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Recent artificial intelligence methods and coronaviruses
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- 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 · 2020: 15
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › 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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