Yujie Chen

Motif-Based Graph Learning for Synthetic Reaction Condition Prediction.

Organic synthetic reactions form the foundation of industrial manufacturing. It is crucial to develop advanced predictive models for synthetic reaction conditions to support the optimization of organic reactions. Synthetic reaction condition is closely related to the molecular motifs (substructures or functional groups), which traditional molecular graphs are unable or difficult to capture. However, many existing studies overlook the significance of motif-level graphs. To address this issue, we propose a novel method for predicting reaction conditions in organic synthesis. First, we extract the chemical features of the reactants and products at both the atom and motif levels, and then uses two branches in the encoder to separately capture local and global contexts and then integrate them. Next, a cross-attention module is employed to learn the latent relationships between the reactants and products, enriching the reaction representation. Experimental results demonstrate that our method outperforms the strongest baseline with up to 30% improvement in Top-10 accuracy on the USPTO_CONDITION data set. Attention analysis further reveals that our method effectively captures critical motifs closely related to synthetic reaction conditions and exhibits interpretable capabilities. The code for MGLSRC is available at: https://github.com/Z-dot-max/MGLSRC.

Beyond Reaction Data: Learning Chemical Knowledge from Large-Scale Molecules for Retrosynthesis

Retrosynthesis, the process of predicting reactants from products, remains a critical challenge in computational chemistry and drug discovery. While recent deep learning methods have shown strong performance, they remain overly reliant on reaction datasets, which are limited in availability and quality. Large-scale unlabeled molecular data encode rich structural patterns that can be leveraged to learn transferable chemical knowledge, but remain largely unexplored. In this work, we propose KnowRetro (Knowledge-Guided Retrosynthesis Prediction), a chemically-aware framework that learns chemical knowledge from large-scale unlabeled molecules to enhance the accuracy and diversity of retrosynthesis prediction. Specifically, KnowRetro first builds a hierarchical knowledge graph from millions of unlabeled molecules, which captures transformation-relevant relationships among molecules, substructures, and functional groups. It then employs chemically guided pre-training based on substructure decomposition to encourage the model to capture fundamental reaction patterns, followed by fine-tuning with an adapter designed to inject task-relevant knowledge into reactant generation. Extensive experiments demonstrate that KnowRetro achieves high accuracy with improved robustness and diversity in reactant generation. Our code is available at https://github.com/chenyujie1127/KnowRetro.

Toward Synthesizability-Aware Multi-Step Retrosynthetic Planning