Retrosynthetic planning plays an important role in the field of organic chemistry, which could generate a synthetic route for the target product. The synthetic route is a series of reactions which are started from the available molecules. The most challenging problem in the generation of the synthetic route is the large search space of the candidate reactions. Estimating the cost of candidate reactions has been proved effectively to prune the search space, which could achieve a higher accuracy with the same search iteration. And the estimation of one reaction is comprised of the estimations of all its reactants. So, how to estimate the cost of these reactants will directly influence the quality of results. To get a better performance, we propose a new framework, named GNN-Retro, for retrosynthetic planning problem by combining graph neural networks(GNN) and the latest search algorithm. The structure of GNN in our framework could incorporate the information of neighboring molecules, which will improve the estimation accuracy of our framework. The experiments on the USPTO dataset show that our framework could outperform the state-of-the-art methods with a large margin under the same settings.
|Title of host publication
|Proceedings of the AAAI Conference on Artificial Intelligence
|Association for the Advancement of Artificial Intelligence (AAAI)
|Number of pages
|Published - Jun 28 2022
Bibliographical noteKAUST Repository Item: Exported on 2022-09-21
Acknowledged KAUST grant number(s): BAS/1/1635-01-01
Acknowledgements: The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) under award number BAS/1/1635-01-01, and the NSFC (U2001212, 62032001, and 61932004). Bin Yao was supported by the NSFC (61922054, 61872235, 61832017, and 61729202), and the National Key Research and Development Program of China (2020YFB1710202, and 2018YFC1504504).