Log-RRIM: Yield Prediction via Local-to-Global Reaction Representation Learning and Interaction Modeling.

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. A 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

SUBMITTER: Hu X 

PROVIDER: S-EPMC12344698 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature