arXiv:2602. 13136v2 Announce Type: replace Abstract: Template-free retrosynthesis methods treat the task as black-box sequence generation, limiting learning efficiency, while semi-template approaches rely on rigid reaction libraries that constrain generalization.
By Chenguang Wang, Zihan Zhou, Lei Bai, Tianshu Yu
MAELLE is a mechanistic reaction prediction framework that models chemical reactions as discrete flow matching over graph-structured electron occupation vectors. It formulates the reactant-to-product mapping as a Continuous-time Markov Chain on electron sites and uses Optimal Transport to generate mechanistically interpretable edit trajectories without elementary step annotations. The method achieves competitive accuracy on the USPTO-480K benchmark, remains robust in out-of-distribution scenarios, and can recover mechanistic pathways that align with known chemistry and predict side products.
By Nguyen Xuan-Vu, Octavian Susanu, Daniel Armstrong, Philippe Schwaller
ReCurveflow is a flow‑matching framework that learns to predict transition state geometries by training on continuously curved reference paths derived from full NEB bands, rather than straight linear paths. It introduces an off‑path correction mechanism that generates corrective velocity fields when the model encounters geometries off the training path, improving resistance to exposure bias and TS prediction accuracy. Across multiple data splits and evaluation metrics, ReCurveflow outperforms seven baselines and produces reaction trajectories whose energy profiles closely follow the reference NEB path, aiding NEB optimization and demonstrating effective corrective behavior.
By Seungheun Baek, Mogan Gim, Jaewoo Kang
arXiv:2607. 17033v1 Announce Type: new Abstract: Forecasting the outcomes of transition-metal-catalyzed reactions is notoriously complex due to the interplay of diverse physical and chemical variables.
By Qiwei Han, Chi Zhou
The paper introduces Align-React, a chemical reaction representation learning framework that incorporates atomic correspondence between reactants and products, an adapter for embedding reaction conditions, and a Reaction-Center-Aware attention mechanism. These components enable the model to capture precise molecular transformations and focus on critical functional groups, leading to improved performance across a variety of organic reaction tasks. The framework outperforms existing architectures on most benchmark datasets.
By Kaipeng Zeng, Xianbin Liu, Yu Zhang, Xiaokang Yang, Yaohui Jin, Yanyan Xu
arXiv:2608. 06259v1 Announce Type: new Abstract: Reaction yield prediction remains challenging because labeled data are scarce and reaction space is both combinatorially large and sparsely populated, limiting the generalization of existing reaction representations.
By Yiting Zheng, Cheng Fang, Anthony Donofrio, Haote Li