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Computational Screening Using a Combination of Ligand-Based Machine Learning and Molecular Docking Methods for the Repurposing of Antivirals Targeting the SARS-CoV-2 Main Protease

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Abstract

Background

COVID-19 is an infectious disease caused by SARS-CoV-2, a close relative of SARS-CoV. Several studies have searched for COVID-19 therapies. The topics of these works ranged from vaccine discovery to natural products targeting the SARS-CoV-2 main protease (Mpro), a potential therapeutic target due to its essential role in replication and conserved sequences. However, published research on this target is limited, presenting an opportunity for drug discovery and development.

Method

This study aims to repurpose 10692 drugs in DrugBank by using ligand-based virtual screening (LBVS) machine learning (ML) with Konstanz Information Miner (KNIME) to seek potential therapeutics based on Mpro inhibitors. The top candidate compounds, the native ligand (GC-376) of the Mpro inhibitor, and the positive control boceprevir were then subjected to absorption, distribution, metabolism, excretion, and toxicity (ADMET) characterization, drug-likeness prediction, and molecular docking (MD). Protein–protein interaction (PPI) network analysis was added to provide accurate information about the Mpro regulatory network.

Results

This study identified 3,166 compound candidates inhibiting Mpro. The random forest (RF) molecular access system ML model provided the highest confidence score of 0.95 (bromo-7-nitroindazole) and identified the top 22 candidate compounds. Subjecting the 22 candidate compounds, the native ligand GC-376, and boceprevir to further ADMET property characterization and drug-likeness predictions revealed that one compound had two violations of Lipinski’s rule. Additional MD results showed that only five compounds had more negative binding energies than the native ligand (− 12.25 kcal/mol). Among these compounds, CCX-140 exhibited the lowest score of − 13.64 kcal/mol. Through literature analysis, six compound classes with potential activity for Mpro were discovered. They included benzopyrazole, azole, pyrazolopyrimidine, carboxylic acids and derivatives, benzene and substituted derivatives, and diazine. Four pathologies were also discovered on the basis of the Mpro PPI network.

Conclusion

Results demonstrated the efficiency of LBVS combined with MD. This combined strategy provided positive evidence showing that the top screened drugs, including CCX-140, which had the lowest MD score, can be reasonably advanced to the in vitro phase. This combined method may accelerate the discovery of therapies for novel or orphan diseases from existing drugs.

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Data Availability

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Abbreviations

ADMET:

Absorption, distribution, metabolism, excretion, and toxicity

ANN:

Artificial neural network

BBB:

Blood–brain barrier

DDX39B:

DEAD-box helicase 39B

COX-2:

Cyclooxygenase-2

hERG:

Human ether-a-go-go gene

LBVS:

Ligand-based virtual screening

MACCS:

Molecular access system

MCS:

Maximum common substructure

MD:

Molecular docking

ML:

Machine learning

MPro :

SARS-CoV-2 main protease

OCT2:

Organic cation transporter 2

PPI:

Protein–protein interaction

RF:

Random forest

ROC:

Receiver operating characteristic

SC-558:

1-Phenylsulfonamide-3-trifluoromethyl-5-parabromophenylpyrazole

SMARTS:

SMiles ARbitrary Target Specification

SMILES:

Simplified Molecular-Input Line-Entry System

SMOTE:

Synthetic Minority Over-sampling Technique

SPTBN2:

Nonerythrocytic beta-spectrin 2

SVM:

Support vector machine

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Acknowledgements

The authors thank Badan Penerbit dan Publikasi, Universitas Gadjah Mada for their assistance in writing.

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  1. Laboratory of Macromolecular Engineering, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada Sekip Utara II, 55281, Yogyakarta, Indonesia

    Gusti Putu Wahyunanda Crista Yuda & Adam Hermawan

  2. Master Student of Pharmaceutical Sciences, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Hacettepe University, Ankara, 06100, Turkey

    Naufa Hanif

  3. Laboratory of Advanced Pharmaceutical Sciences. APSLC Building, Faculty of Pharmacy, Universitas Gadjah Mada Sekip Utara II, 55281, Yogyakarta, Indonesia

    Adam Hermawan

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  1. Gusti Putu Wahyunanda Crista Yuda
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  2. Naufa Hanif
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GPWCY contributed to the design, acquisition, the writing and revision of the article, drafted the article, and the finalized the version to be published. NH contributed to the data acquisition, writing and revision of the article. AH contributed to supervision, review and evaluation of the data and the final approval of the version to be published. This manuscript is a part of the bachelor thesis of GPWCY.

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Correspondence to Adam Hermawan.

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Yuda, G.P.W.C., Hanif, N. & Hermawan, A. Computational Screening Using a Combination of Ligand-Based Machine Learning and Molecular Docking Methods for the Repurposing of Antivirals Targeting the SARS-CoV-2 Main Protease. DARU J Pharm Sci 32, 47–65 (2024). https://doi.org/10.1007/s40199-023-00484-w

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