LoRA-Chem: Modular Machine Learning for Multitask Prediction in Organic Reactions

Open AccessCCS ChemistryRESEARCH ARTICLES13 Sep 2025LoRA-Chem: Modular Machine Learning for Multitask Prediction in Organic Reactions Ben Gao, Penghui Li, Di Zhang, Qian Tan, Wanhao Liu, Xunzhi Wang, JunXian Li, Shufei Zhang, Dongzhan Zhou, Yuqiang Li and Guoyin Yin Ben Gao , Penghui Li , Di Zhang , Qian Tan , Wanhao Liu , Xunzhi Wang , JunXian Li , Shufei Zhang , Dongzhan Zhou , Yuqiang Li and Guoyin Yin https://doi.org/10.31635/ccschem.025.202506542 SectionsSupplemental MaterialAboutPDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked InEmail The prediction of organic chemical reactions has historically presented significant challenges owing to the inherent complexity and mechanistic diversity of reaction processes. In this work, we present LoRA-Chem, an innovative modular framework that demonstrates remarkable performance in predicting individual organic reactions while achieving unprecedented multitask capacity. This unified architecture enables accurate forecasting of diverse reaction types through a single integrated model - a critical advancement that addresses longstanding limitations in traditional prediction methodologies. Importantly, our investigation reveals that strategic improvements in the foundational model architecture can substantially enhance the system's predictive accuracy. The demonstrated versatility and scalability of the LoRA-Chem framework suggest transformative potential for next-generation reaction prediction systems, offering a robust platform for developing sophisticated artificial intelligence solutions in synthetic chemistry. Download figure Download PowerPoint Previous articleNext article FiguresReferencesRelatedDetails Issue AssignmentNot Yet AssignedSupporting Information Copyright & Permissions© 2025 Chinese Chemical Society Downloaded 7 times PDF downloadLoading ...