Molecular Fragment Encoding via Rooted Traversal for Unique Results
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Solution Overview
Problem
Existing molecule encoding methods face challenges with uncertainty, computational complexity, and inefficiency due to manual rule definition and graph matching, leading to inconsistencies and errors, especially in complex molecular structures.
Innovation Solution
A method involving determining a root node for a molecular fragment structure, traversing the structure to obtain traversal record information, and using a specified encoding rule to generate an encoding result, which avoids manual errors and graph parsing, ensuring unique and accurate encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual rule definition and graph matching methods are used for molecule encoding, then encoding flexibility and adaptability are maintained, but encoding accuracy decreases and computational complexity increases
Solution Approach 1:
The patent replaces manual rule definition and graph matching operations with an automated neural network-based encoding system. The molecular graph is directly processed by the neural network to generate encodings, eliminating the need for manual SMARTS rule creation and graph parsing steps. This substitution of mechanical/manual operations with automated intelligent processing resolves the contradiction by improving accuracy through consistent automated application while managing complexity through algorithmic efficiency.
Solution Approach 2:
The neural network system performs self-learning and self-optimization to improve encoding accuracy without requiring manual intervention for rule definition. The system automatically adapts to different molecular structures through training on labeled data, eliminating the need for continuous manual rule updates and graph matching adjustments. This self-service capability improves reliability while containing computational complexity through efficient deep learning architectures.
2Adaptability or versatility
If manual rule definition and graph matching are used, then encoding adaptability to different molecular structures is maintained, but time consumption increases
Solution Approach 1:
The patent employs preliminary training of neural network models on extensive molecular structure datasets before actual encoding tasks. This pre-learning phase enables the system to quickly adapt to different molecular structures during inference without requiring time-consuming manual rule definition or graph matching for each new molecule. The pre-trained models capture general molecular patterns that can be rapidly applied across diverse chemical structures.
Solution Approach 2:
The system achieves adaptability through parameter adjustments in the neural network architecture and training configuration rather than through manual rule changes. Different molecular structure types are handled by modifying network parameters and training data composition rather than creating new encoding rules, significantly reducing the time required to adapt to different molecular families while maintaining versatility.
3Measurement precision
If graph parsing and graph matching operations are performed, then complete molecular structure analysis is achieved, but computational efficiency decreases
Solution Approach 1:
The patent replaces the mechanical graph parsing and graph matching pipeline with a direct neural network processing approach. The neural network takes the molecular graph as input and directly produces encodings without explicit graph parsing or matching steps. This substitution maintains structural analysis completeness through the network's ability to learn structural features while dramatically improving encoding efficiency through optimized computational operations.
Solution Approach 2:
The patent segments the molecular structure processing into hierarchical levels within the neural network, where different network layers capture different structural features (atoms, bonds, functional groups, molecular scaffolds). This segmentation allows complete structure analysis to be distributed across multiple processing stages, improving overall encoding efficiency by parallelizing feature extraction while maintaining comprehensive structural understanding.
Data Source
AI summary
Embodiments in the disclosure provide a method and an apparatus for molecule encoding, a device, and a readable storage medium. The method includes determining, based on a molecular fragment structure of a target molecule, a root node corresponding to the molecular fragment structure; traversing the molecular fragment structure based on the root node to obtain traversal record information corresponding to the molecular fragment structure, the traversal record information corresponding to the molecular fragment structure indicating attribute information of each atom traversed along a traversal path corresponding to the traversal; and determining, by performing encoding on the traversal record information corresponding to the molecular fragment structure using a specified encoding rule, an encoding result corresponding to the molecular fragment structure, the encoding result including encoded representations of a plurality of atoms in the molecular fragment structure. In this way, the applicable scenarios of molecular encoding can be improved.