Authors: Kaipeng Zeng, Xianbin Liu, Yu Zhang, Xiaokang Yang, Yaohui Jin, Yanyan Xu
Abstract: Organic synthesis stands as a cornerstone of chemical industry. The
development of robust machine learning models to support tasks associated with
organic reactions is of significant interest. However, current methods rely on
hand-crafted features or direct adaptations of model architectures from other
domains, which lacks feasibility as data scales increase or overlook the rich
chemical information inherent in reactions. To address these issues, this paper
introduces {\modelname}, a novel chemical reaction representation learning
model tailored for a variety of organic-reaction-related tasks. By integrating
atomic correspondence between reactants and products, our model discerns the
molecular transformations that occur during the reaction, thereby enhancing the
comprehension of the reaction mechanism. We have designed an adapter structure
to incorporate reaction conditions into the chemical reaction representation,
allowing the model to handle diverse reaction conditions and adapt to various
datasets and downstream tasks, e.g., reaction performance prediction.
Additionally, we introduce a reaction-center aware attention mechanism that
enables the model to concentrate on key functional groups, thereby generating
potent representations for chemical reactions. Our model has been evaluated on
a range of downstream tasks, including reaction condition prediction, reaction
yield prediction, and reaction selectivity prediction. Experimental results
indicate that our model markedly outperforms existing chemical reaction
representation learning architectures across all tasks. Notably, our model
significantly outperforms all the baselines with up to 25\% (top-1) and 16\%
(top-10) increased accuracy over the strongest baseline on USPTO\_CONDITION
dataset for reaction condition prediction. We plan to open-source the code
contingent upon the acceptance of the paper.
Source: http://arxiv.org/abs/2411.17629v1
