Integrated machine learning-driven QSAR modelling and systems biology approach for the identification of potential SARS-CoV-2 3CLpro inhibitors

Q: What does this research mean for the field?

An integrated computational pipeline combining machine learning-driven QSAR modeling and systems biology can identify potential SARS-CoV-2 3CLpro inhibitors and elucidate their mechanistic links to antiviral and immune processes. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Ajay Manaithiya; R. K. Bhowmik; Ranjan Ray; Sunil Kumar; Shweta Sharma; Bijo Mathew; Wuming Gong; Ashok Aspatwar

doi:10.1080/1062936x.2025.2584058

November 2, 2025SAR and QSAR in environmental researchOpen Access

Integrated machine learning-driven QSAR modelling and systems biology approach for the identification of potential SARS-CoV-2 3CLpro inhibitors

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Key Points

The aim is to discover and validate inhibitors of the SARS-CoV-2 3CLpro enzyme using a computational approach.
Utilized machine learning algorithms for QSAR modelling to identify potential inhibitors.
Integrative systems biology approach to connect compound action with antiviral and immune responses.
Developed a computational pipeline for accelerated drug discovery.
Identified key nodes linking compounds to antiviral and immune processes.
Generated a shortlist of potential 3CLpro inhibitors for further experimental validation.

Implication

Identifies potential SARS-CoV-2 3CLpro inhibitors using machine learning, suggesting new antiviral candidates.

Abstract

, were identified as major nodes linking compound action to antiviral and immune processes. This study delivers a combinatorial computational pipeline to accelerate the discovery of 3CLpro-targeted antivirals, providing mechanistic insights and a shortlist of candidates for future experimental validation.

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Cite This Study

Manaithiya et al. (2025) studied this question.

synapsesocial.com/papers/6a20b8569e00afa23b234aa3 https://doi.org/10.1080/1062936x.2025.2584058

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Integrated machine learning-driven QSAR modelling and systems biology approach for the identification of potential SARS-CoV-2 3CLpro inhibitors

Key Points

Implication

Abstract

Discussion

Cite This Study

Integrated machine learning-driven QSAR modelling and systems biology approach for the identification of potential SARS-CoV-2 3CLpro inhibitors

Key Points

Implication

Abstract

Discussion

Cite This Study

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