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Updated: May 25, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
Published on: December 1, 2020
Binding Affinity Prediction and Pesticide Screening against Phytophthora sojae Using a Heterogeneous Interaction
Youxu Dai1, Aiping Han1, Huijun Ma2
1School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing 100048, China.
A novel graph neural network model identified potential pesticides for soybean root rot. Compounds MOL011832 and MOL011833 from Schizonepeta herb showed promise, validated by biological experiments.
Area of Science:
- Agricultural Science
- Computational Chemistry
- Biotechnology
Background:
- Soybean root and stem rot causes significant global economic losses.
- Developing effective and novel pesticides is crucial for crop protection.
Purpose of the Study:
- To develop and validate a machine learning model for discovering new pesticides against Phytophthora sojae.
- To identify potential drug candidates from natural sources for controlling soybean root rot.
Main Methods:
- Constructed a heterogeneous interaction graph attention network model using PDBbind data.
- Screened 14,249 compounds from the Traditional Chinese Medicine Systems Pharmacology database against Phytophthora sojae chitin synthase (PsChs1).
- Validated top candidates (MOL011832, MOL011833) using molecular docking, molecular dynamics simulations, and biological assays with Schizonepeta extract.
Main Results:
- Identified MOL011832 and MOL011833 as potential pesticides targeting PsChs1.
- Both compounds were found in the herb Schizonepeta.
- Ethanol extract of Schizonepeta demonstrated confirmed inhibitory effects against P. sojae.
Conclusions:
- Graph neural network-based models are effective and efficient for pesticide discovery.
- Schizonepeta contains compounds with potential for controlling soybean root and stem rot.
- This approach accelerates the identification of novel crop protection agents.
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