Molecule Completion Model for Reactant Prediction
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Solution Overview
Problem
Current methods for predicting reactant molecules in chemical synthesis and drug preparation rely on synthesis reaction templates, which are difficult to generalize to new reaction types, costly to update, and lack global correctness, leading to inaccurate predictions.
Innovation Solution
A method and apparatus using a molecule completion model trained on sample compound molecules with masked sub-structures, allowing for self-supervised learning that predicts reactant molecules without relying on known synthesis reactions, enhancing generalization and prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If synthesis reaction templates are used to predict reactant molecules, then prediction can be performed based on known reactions, but the method lacks generalization ability to new reaction types and requires costly frequent updates
Solution Approach 1:
The system performs self-supervised learning by automatically generating training data from compound molecules through masking operations. The model learns to predict masked sub-structures without requiring external synthesis reaction templates, enabling it to serve itself in learning chemical reaction patterns and improving generalization to new reaction types
Solution Approach 2:
The approach transforms the prediction problem by changing the training paradigm from template-based to completion-based. By modifying how the model is trained (using masked compound molecules instead of reaction templates), the system achieves better adaptability while maintaining prediction reliability
2Reliability
If synthesis reaction templates are used for reactant prediction, then predictions can be made using existing knowledge, but the method lacks global correctness and accuracy
Solution Approach 1:
The compound molecules are segmented into sub-structures through masking operations. By training the model to predict these masked sub-structures, the system learns local structural patterns that contribute to global molecular correctness, improving overall prediction accuracy
Solution Approach 2:
The model performs preliminary learning of molecular structures through self-supervised training on masked compound molecules before being applied to reactant prediction. This preliminary action of learning structural patterns improves the model's ability to maintain global correctness in predictions
3Adaptability or versatility
If synthesis reaction templates are updated frequently to include new reactions, then coverage of reaction types improves, but the cost and complexity of maintenance increases
Solution Approach 1:
Instead of requiring external updates to synthesis reaction templates, the system performs self-supervised learning by automatically generating training examples from compound molecules. This self-service mechanism eliminates the need for manual template maintenance while maintaining broad coverage of reaction types
Solution Approach 2:
The model trained on masked compound molecules achieves universal applicability across different reaction types without requiring separate templates for each reaction. The self-supervised learning approach creates a universal predictor that handles diverse reactions, reducing maintenance complexity
Data Source
AI summary
A method for predicting reactant molecules includes obtaining a product molecule, and selecting bonds of the product molecule to be broken to obtain a molecule to be completed, the product molecule defining a compound molecule of a reactant molecule to be predicted. The method further includes applying a molecule completion model to complete the molecule to be completed to obtain a completion result indicating a reactant molecule of the product molecule based on the molecule to be completed. The molecule completion model is obtained by training based on sample compound molecules and sample molecules to be completed obtained by masking sub-structures in the sample compound molecules.