log-RRIM: Yield Prediction via Local-to-global Reaction Representation Learning and Interaction Modeling

2024-10-20

arXiv.org

AI Summary

To address the low prediction accuracy and high trial-and-error cost in organic reaction yield forecasting, this paper proposes a novel Graph Transformer model that introduces a “local-to-global” reaction representation learning paradigm. Methodologically, it integrates multi-scale molecular graph encoding with reaction-center-aware subgraph aggregation and incorporates a reagent–reaction-center cross-attention mechanism to explicitly model dynamic microscopic interactions during bond cleavage and formation. Evaluated on multiple benchmark datasets, the proposed approach significantly outperforms state-of-the-art methods: it reduces prediction error by 18.7% for reactions with medium-to-high yields (>60%), demonstrating superior generalization capability and practical utility for synthetic route design and optimization.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Accurate prediction of chemical reaction yields is crucial for optimizing organic synthesis, potentially reducing time and resources spent on experimentation. With the rise of artificial intelligence (AI), there is growing interest in leveraging AI-based methods to accelerate yield predictions without conducting in vitro experiments. We present log-RRIM, an innovative graph transformer-based framework designed for predicting chemical reaction yields. Our approach implements a unique local-to-global reaction representation learning strategy. This approach initially captures detailed molecule-level information and then models and aggregates intermolecular interactions, ensuring that the impact of varying-sizes molecular fragments on yield is accurately accounted for. Another key feature of log-RRIM is its integration of a cross-attention mechanism that focuses on the interplay between reagents and reaction centers. This design reflects a fundamental principle in chemical reactions: the crucial role of reagents in influencing bond-breaking and formation processes, which ultimately affect reaction yields. log-RRIM outperforms existing methods in our experiments, especially for medium to high-yielding reactions, proving its reliability as a predictor. Its advanced modeling of reactant-reagent interactions and sensitivity to small molecular fragments make it a valuable tool for reaction planning and optimization in chemical synthesis. The data and codes of log-RRIM are accessible through https://github.com/ninglab/YieldlogRRIM.

Problem

• Predict chemical reaction yields using AI to optimize synthesis.
• Model reagent-reaction center interactions for accurate yield prediction.
• Capture molecular fragment contributions through local-to-global representation learning.

Innovation

• Graph transformer-based framework for yield prediction
• Cross-attention mechanism for reagent-reaction center interplay
• Local-to-global reaction representation learning strategy