Robust Chemical Reaction Condition Recommendations via Label Mix Strategy

Area of Science:

• Artificial intelligence in chemistry
• Machine learning for chemical reactions
• Computational chemistry

Background:

• Recommending optimal reaction conditions is a significant challenge in AI-driven chemistry.
• Limited representation of condition features and sparse labeled data hinder model performance.
• Existing methods struggle with noisy and incomplete datasets.

Purpose of the Study:

• To develop an advanced AI framework for predicting optimal reaction conditions.
• To address the limitations of feature representation and data sparsity in chemical reaction prediction.
• To enhance the accuracy, generalization, and robustness of AI models in chemistry.

Main Methods:

• Utilizing a collaborative filtering framework to encode reaction conditions.
• Employing graph neural networks (GNNs) for condition feature representation.
• Implementing a differential cross-attention mechanism to capture reaction-condition interactions.
• Introducing a Label Mix strategy and co-occurrence matrix regularization to mitigate data noise and sparsity.

Main Results:

• Demonstrated significant improvements in accuracy across multiple benchmark datasets.
• Showcased enhanced generalization capabilities in both full-data and few-shot learning scenarios.
• Validated the robustness of the proposed framework against noisy and sparse data.

Conclusions:

• The proposed AI framework effectively addresses key challenges in recommending optimal reaction conditions.
• The integration of GNNs and differential cross-attention offers a powerful approach for AI-driven chemistry.
• The Label Mix strategy and regularization techniques improve model performance and reliability.