ChemBART is a BART‑based language model trained on holistic reaction expressions (Reactant > Reagent > Product) using a mask‑filling objective. It can solve diverse chemical tasks such as precursor/reagent generation, temperature‑yield regression, and property classification, and it is integrated with Monte Carlo tree search to design multi‑step synthesis routes. The model’s predictions were experimentally validated, yielding shorter synthetic pathways with over 20 % higher yield than literature benchmarks.
By Kenan Li, Yijian Zhang, Jin Wang, Haipeng Gan, Zeying Sun, Xiaoguang Lei, Hao Dong
arXiv:2604.07669v3 Announce Type: replace-cross
Abstract: Synthesizable molecular optimization seeks to improve target properties while ensuring that molecular modifications follow feasible synthetic...
By Tao Li, Kaiyuan Hou, Tuan Vinh, Fanglei Xue, Monika Raj, Zhichun Guo, Carl Yang
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
The paper introduces Top‑K prompting as a training and inference strategy to better capture the diverse, plausible predictions inherent in single‑step retrosynthesis. Using an ultra‑large dataset (CREED‑CCV‑2+USPTO‑XL) of ~45.6 million verified reactions, the authors train the Chemistry Constraint‑Consistent Language Model (C3LM). With fine‑tuning that incorporates ChemCensor‑based and novelty‑oriented rewards, C3LM achieves state‑of‑the‑art performance on the OOD URSA‑expert‑2026 benchmark and demonstrates complementary reaction space exploration compared to conventional models, suggesting benefits for ensemble‑based retrosynthesis systems.
By Bogdan Zagribelnyy, Ivan Ilin, Nikita Bondarev, Maksim Kuznetsov, Mathieu Reymond, Vladimir Aladinskiy, Alex Aliper, Alex Zhavoronkov
RxnOptBench is a new benchmark that tests large language models on the task of selecting optimal reaction conditions—such as catalyst, ligand, solvent, temperature, and atmosphere—by reading real condition‑screening tables from 2025 organic‑methodology papers. The benchmark uses a continuous relative score that combines yield with stereochemical metrics (ee, dr, rr) and includes a design that separates in‑context literature use from mere memorization. Evaluation across nine frontier LLMs and three chemistry‑specialized LLMs shows that even the best models still have significant room for improvement, with chemistry‑specialized models performing at random levels on multi‑axis selection while open‑weight models approach proprietary models.
By Lingli Ge, Yubin Wang, Junyuan Gao, Jiahe Song, Jiaxing Sun, Boyu Zhu, Haote Yang, Jingchao Wang, Lixin Ma, Jiang Wu, Yuqiang Li, Conghui He
arXiv:2607. 12771v1 Announce Type: new Abstract: Reaction mechanisms consist of the step-by-step sequences of elementary reactions that explain chemical transformations.
By Xingyu Dang, Haocheng Tang, Junmei Wang, Yanjun Li