Ligand Graph Screening Model for Accurate Virtual Screening
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Solution Overview
Problem
Existing molecular fingerprint generation methods in drug discovery require manual feature extraction by developers with deep domain knowledge, limiting the efficiency and accuracy of virtual screening.
Innovation Solution
A ligand screening model construction method using a ligand graph network with random initialization vectors to identify weight vectors, reconstructing nodes, and performing deep learning on multiple layers to construct a ligand screening model, reducing manual feature design and enhancing feature coverage and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual feature extraction methods are used for molecular fingerprint generation, then the method can be implemented with traditional approaches, but the requirement for developer domain knowledge increases and efficiency decreases
Solution Approach 1:
The patent replaces manual mechanical feature extraction with an automated graph neural network system. The molecular structure is represented as a graph where atoms are nodes and bonds are edges, and the GNN automatically learns features through message passing and aggregation operations, eliminating the need for manual fingerprint generation and reducing dependency on developer domain knowledge
Solution Approach 2:
The graph neural network performs self-service by automatically learning and extracting features from molecular graphs without human intervention. The model iteratively updates node representations through message passing and aggregation, enabling the system to improve its feature extraction capability autonomously during training
2Ease of manufacture
If manual feature extraction is used, then the implementation is simpler, but the accuracy of virtual screening predictions decreases
Solution Approach 1:
The patent changes the parameter representation from fixed manual fingerprints to dynamic graph-based representations. The molecular graph structure preserves topological information and enables the model to learn hierarchical features at different levels, improving prediction accuracy while maintaining implementation feasibility through standardized graph neural network architectures
Solution Approach 2:
The patent adds a structural dimension by representing molecules as graphs with nodes and edges, rather than using flat fingerprint vectors. This dimensional transformation enables the model to capture spatial relationships and structural patterns that are lost in traditional fingerprint methods, thereby improving prediction accuracy
3Device complexity
If traditional molecular fingerprint methods are used, then the computational approach is simpler, but the feature coverage is limited
Solution Approach 1:
The graph neural network model serves multiple functions: it can extract structural features, predict molecular properties, and generate representations suitable for various downstream tasks. The same GNN architecture can be applied to different molecular datasets and prediction tasks, providing universal feature extraction capability that exceeds the limited scope of traditional fingerprints
Data Source
AI summary
A ligand screening model construction method, a ligand screening model construction device, and a drug ligand screening method for drug screening, comprising that obtain a drug ligand training set, and the drug ligand training set includes a drug ligand chemical formula and a classification label; a ligand graph network is drawn, in which atoms are nodes and chemical bonds are edges connecting nodes; use a random initialization vector to identify the weight vector of each node in the ligand graph network; reconstruct each node of the ligand graph network to obtain a reconstruction network, and repeat the reconstruction steps to obtain at least two layers of reconstruction networks; perform deep learning on the ligand graph network and the at least two-layer reconstruction graph network according to the classification labels, and construct a ligand screening model.