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wSDTNBI: a novel network-based inference method for virtual screening.

Zengrui Wu¹, Hui Ma¹, Zehui Liu¹

¹Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology 130 Meilong Road Shanghai 200237 China ytang234@ecust.edu.cn huangjin@ecust.edu.cn.

Chemical Science

|February 25, 2022

Summary

We developed wSDTNBI, a novel network-based virtual screening method using weighted drug-target interaction networks. This approach identified new RORγt inverse agonists, including ursonic acid, with therapeutic potential for multiple sclerosis.

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Area of Science:

Computational chemistry
Pharmacology
Bioinformatics

Background:

Network-based virtual screening (VS) is emerging for drug discovery.
Existing methods often use unweighted drug-target interaction (DTI) networks.

Purpose of the Study:

To introduce wSDTNBI, a novel network-based inference method for VS.
To utilize weighted DTI networks correlated with binding affinities for improved predictions.

Main Methods:

Developed wSDTNBI, employing a two-pronged approach with weighted DTI and drug-substructure association networks.
Applied wSDTNBI for network-based VS targeting retinoid-related orphan receptor γt (RORγt).
Experimentally validated predicted compounds through in vitro and in vivo studies.

Main Results:

Identified seven novel RORγt inverse agonists from 72 tested compounds.
Ursonic acid and oleanonic acid showed potent inhibition (IC50: 10 nM and 0.28 μM, respectively).
Confirmed direct interaction of ursonic acid with RORγt via X-ray crystallography; demonstrated therapeutic effects in multiple sclerosis models.

Conclusions:

wSDTNBI is a powerful tool for network-based VS in drug discovery.
Identified promising drug candidates for RORγt-related diseases.
Demonstrated the therapeutic potential of ursonic acid and oleanonic acid for multiple sclerosis.