MOTIVATION: Organic synthesis is fundamental to the chemical industry, particularly in domains such as pharmaceutical development. While artificial intelligence offers powerful tools for modeling chemical reactions, current approaches are primarily limited to two paradigms: those that rely on hand-crafted, domain-specific features, and those that apply generic deep learning models through simplistic concatenation or aggregation of reaction components. The former often struggles to scale effectively with increasing data volumes, while the latter relies on simple input- or feature-level concatenation to combine different reaction components. Such simplistic aggregation prevents these models from directly capturing the structural transformations between reactants and products, and also makes it difficult to adapt or extend them to datasets that include non-molecular reaction conditions without modifying their model architectures. RESULTS: This paper introduces AlignReact, a novel chemical reaction representation learning framework designed for a wide range of organic reaction tasks. Our approach integrates atomic correspondence between reactants and products to discern precise molecular transformations, thereby improving the model's comprehension of molecular transformation patterns. We incorporate an adapter structure to embed reaction conditions into the representation, enhancing adaptability across varied datasets and tasks. Furthermore, a Reaction-Center-Aware attention mechanism is proposed to enable the model to focus on critical functional groups, yielding more powerful and informative representations. Evaluated across multiple downstream tasks, our model demonstrates superior performance, significantly outperforming existing chemical reaction representation learning architectures on most benchmark datasets. SCIENTIFIC CONTRIBUTION: We introduce a chemical reaction representation learning framework that explicitly integrates atomic correspondence between reactants and products into the network architecture, enabling the model to perceive and model molecular structural transformations during reactions. As an extensible but preliminary feature, our approach also features a flexible, detachable module for integrating reaction conditions. Inspired by conditioning mechanisms from multimodal generation, this adapter module accepts precomputed features of varying dimensions and modalities, laying the groundwork for broader dataset compatibility compared to prior work, and enables fine-grained, context-aware conditioning. Extensive experiments on a range of downstream datasets demonstrate that our framework achieves state-of-the-art performance across several key chemical reaction prediction tasks.
Zeng et al. (Tue,) studied this question.