{"type":"ScholarlyArticle","title":"RetroMoE: A Mixture-of-Experts Latent Translation Framework for Single-step Retrosynthesis","authors":[{"name":"Xinjie Li","slug":"xinjie-li","given":"Xinjie","family":"Li","affiliation":""},{"name":"Abhinav Verma","slug":"abhinav-verma","given":"Abhinav","family":"Verma","affiliation":""}],"abstract":"Single-step retrosynthesis is a crucial task in organic synthesis, where the objective is to identify the reactants needed to produce a given product. In recent years, a variety of machine learning methods have been developed to tackle retrosynthesis prediction. In our study, we introduce RetroMoE, a novel generative model designed for the single-step retrosynthesis task. We start with a non-symmetric variational autoencoder (VAE) that incorporates a graph encoder to map molecular graphs into a latent space, followed by a transformer decoder for precise prediction of molecular SMILES strings. Additionally, we implement a simple yet effective mixture-of-experts (MoE) network to translate the product latent embedding into the reactant latent embedding. To our knowledge, this is the first approach that frames single-step retrosynthesis as a latent translation problem. Extensive experiments on the USPTO-50K and USPTO-MIT datasets demonstrate the superiority of our method, which not only surpasses most semi-template-based and template-free methods but also delivers competitive results against template-based methods. Notably, under the class-known setting on the USPTO-50K, our method achieves top-1 exact match accuracy comparable to the state-of-the-art template method, RetroKNN.","doi":"10.24963/ijcai.2025/835","url":"https://ijcai.org/proceedings/2025/835","pdf":"https://ijcai.org/proceedings/2025/0835.pdf","markdown":"https://ijcai.org/proceedings/2025/835.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/835","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":835,"pages":{"first":7509,"last":7517},"keywords":["Multidisciplinary Topics and Applications: MTA: Bioinformatics","Multidisciplinary Topics and Applications: MTA: Health and medicine","Multidisciplinary Topics and Applications: MTA: Physical sciences","Multidisciplinary Topics and Applications: MTA: Life sciences"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}