Graph Neural Network Drug Screening Model
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Solution Overview
Problem
Conventional drug screening methods are costly, time-consuming, and have a low success rate due to inefficiencies in assessing the activities and properties of substances for potential drug use.
Innovation Solution
A drug screening method utilizing a graph neural network (GNN) to determine structural features of protein and target molecules, predicting activity values by concatenating node features, and efficiently processing large datasets to identify promising drug-target interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual experiments are conducted for drug screening, then the assessment of substance activities can be performed, but the cost is high and the research and development cycle is long
Solution Approach 1:
The patent creates a virtual copy of the drug screening process using graph neural networks. Instead of physically testing substances in laboratories, the system generates computational models that replicate molecular interactions, allowing rapid assessment of substance activities without time-consuming manual experiments while maintaining assessment accuracy through sophisticated algorithms.
Solution Approach 2:
The patent replaces the mechanical and manual experimental system with an automated computational system. Graph neural networks process molecular structures and predict activities algorithmically, substituting physical laboratory procedures with digital simulations that execute faster and at lower cost while preserving the essential assessment function.
2Measurement precision
If manual experiments are conducted for drug screening, then substance activities can be assessed, but the success rate is low
Solution Approach 1:
The patent performs preliminary computational screening of substances before actual drug development. The graph neural network models predict which substances are most likely to succeed based on their molecular characteristics, allowing researchers to prioritize promising candidates and avoid pursuing ineffective ones, thereby increasing the overall success rate of drug screening while maintaining accurate assessment of substance activities.
3Measurement precision
If conventional drug screening processes are used, then substance assessment can be performed, but the process is inefficient
Solution Approach 1:
The patent fundamentally changes the parameters of the screening process by transitioning from physical experimentation to computational modeling. This parameter change enables parallel processing of multiple substances simultaneously, dramatically increasing screening efficiency while maintaining the capability to accurately assess substance activities through sophisticated graph neural network algorithms that analyze molecular structures and predict interactions.
Data Source
AI summary
This disclosure provides a drug screening method and apparatus, an electronic device, and a computer-readable storage medium. The method includes: determining a structural feature of a protein molecule and a structural feature of a target molecule; obtaining a concatenated node feature corresponding to the protein molecule and the target molecule based on a node information passing sub-network in a drug screening model, the structural feature of the protein molecule, and the structural feature of the target molecule, the node information passing sub-network being a graph neural network; and predicting a first predicted activity value after the protein molecule and the target molecule are bound according to the concatenated node feature.