3D Molecular Graph Training for PROTAC Property Prediction
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Solution Overview
Problem
Current methods lack an effective way to accurately predict molecular properties of proteolysis targeting chimeras, which are crucial for drug discovery and protein design, particularly in areas like cancer treatment.
Innovation Solution
A model training method that constructs three-dimensional molecular graph data of proteolysis targeting chimeras, using atomic nodes and adjacency matrices, and trains a prediction model to predict properties like lipophilicity and solubility by minimizing deviations between predicted and actual molecular properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used for molecular property prediction, then the process is simple, but the accuracy is insufficient
Solution Approach 1:
The patent transitions from traditional two-dimensional molecular representations to three-dimensional molecular graph data, incorporating spatial coordinates and geometric information. This dimensional enhancement enables the model to capture spatial relationships and molecular conformations, significantly improving prediction accuracy for molecular properties while managing model complexity through structured data organization.
Solution Approach 2:
The patent transforms molecular data from simple connectivity information to comprehensive three-dimensional parameters including atomic coordinates, bond lengths, bond angles, and spatial relationships. By changing the parameter representation from 2D to 3D space, the model achieves higher prediction accuracy for molecular properties such as solubility, permeability, and binding affinity.
2Measurement precision
If complex prediction models are developed, then prediction accuracy improves, but computational efficiency decreases
Solution Approach 1:
The patent segments the molecular system into individual atomic nodes and molecular subgraphs, each processed independently through neural network layers. This segmentation allows parallel computation across different atomic and molecular components, maintaining high prediction accuracy while improving computational efficiency through distributed processing and reduced complexity per computational unit.
Solution Approach 2:
By organizing molecular data in three-dimensional space with explicit coordinate information, the patent enables more efficient spatial queries and distance calculations compared to traditional methods. The structured 3D representation allows the model to process spatial relationships more efficiently, balancing accuracy with computational performance.
3Measurement precision
If three-dimensional molecular graph data is constructed, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent segments the three-dimensional molecular graph into atomic nodes with individual feature vectors and molecular subgraphs with structured relationship data. This segmentation simplifies data processing by breaking down complex 3D molecular structures into manageable units that can be processed independently and then aggregated, maintaining accuracy while reducing overall processing complexity.
Solution Approach 2:
The patent transforms raw molecular data into standardized three-dimensional parameters including atomic coordinates, bond vectors, and geometric features. By establishing consistent parameter representations and coordinate systems, the patent simplifies subsequent data processing operations while capturing the essential spatial information needed for accurate molecular property prediction.
Data Source
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AI summary
The present application provides a model training method and apparatus, and a molecular property information prediction method and apparatus. The method include: obtaining data of a designated proteolysis targeting chimera molecule; according to the data, constructing three-dimensional molecular graph data of the designated proteolysis targeting chimera molecule; inputting the three-dimensional molecular graph data of the designated proteolysis targeting chimera molecule into a prediction model to be trained, so that the prediction model predicts molecular property information of the designated proteolysis targeting chimera molecule; and according to a deviation between predicted molecular property information and actual molecular property information of the designated proteolysis targeting chimera molecule, training the prediction model.