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Designing Novel Compound Candidates Against SARS-CoV-2 Using Generative Deep Neural Networks and Cheminformatics.

Shang-Yang Li1, Chin-Mao Hung2,3, Hsin-Yi Hung4

  • 1Graduate Institute of Public Health, College of Public Health, National Defense Medical University, Taipei City 114201, Taiwan.

International Journal of Molecular Sciences
|December 30, 2025
PubMed
Summary
This summary is machine-generated.

Researchers used deep generative models and cheminformatics to discover new potential COVID-19 drugs. A novel compound, Molecule_36, showed promise exceeding Molnupiravir in preclinical assessments, warranting further investigation.

Keywords:
SARS-CoV-2cheminformaticsdrug designgenerative deep neural networksmolnupiravirreinforcement learning

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Area of Science:

  • Computational chemistry and drug discovery
  • Artificial intelligence in medicine
  • Virology and infectious diseases

Background:

  • The COVID-19 pandemic necessitates urgent development of effective antiviral therapies.
  • Existing treatments for SARS-CoV-2 have limitations, driving the search for novel drug candidates.
  • Small molecule inhibitors targeting viral replication are crucial for pandemic control.

Purpose of the Study:

  • To design and screen novel small molecule compounds with potential anti-SARS-CoV-2 activity using deep generative models and cheminformatics.
  • To identify a new drug candidate with improved efficacy and safety profiles compared to existing treatments.
  • To explore the potential of artificial intelligence in accelerating drug discovery for emerging infectious diseases.

Main Methods:

  • Utilized deep generative models and reinforcement learning to generate novel molecular structures.
  • Employed similarity searching against the BIOVIA Available Chemicals Directory (BIOVIA ACD) based on Molnupiravir.
  • Applied cheminformatics techniques, including ADMET analysis, to assess the drug-likeness and potential efficacy of generated compounds.

Main Results:

  • Generated 6,000 small molecule structures using a deep generative model trained on Molnupiravir-like compounds.
  • Screened 38 compounds for potential anti-SARS-CoV-2 activity, with one compound, Molecule_36, passing ADMET analysis.
  • Molecule_36 demonstrated potential for higher affinity with SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) and favorable ADMET properties compared to Molnupiravir.

Conclusions:

  • The combination of generative deep neural networks and cheminformatics is effective for discovering novel anti-SARS-CoV-2 compounds.
  • Molecule_36 represents a promising, unpatented drug candidate for further development against COVID-19.
  • Further experimental validation is required to confirm the stability, mechanism of action, and antiviral efficacy of Molecule_36.