Tengfei Ma

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

Learning Molecular Representations from Cellular Phenotypes with Structure Preservation

Phenotypic drug discovery enables the discovery of functional relationships between molecular structures and cellular responses. However, existing multimodal representation learning methods often optimize cross-modal alignment without considering the intrinsic organization of chemical space, resulting in distorted molecular representations and loss of structural information. We propose \textbf{PhenMol}, a structure-preserving framework for phenotype-aware molecular representation learning. PhenMol disentangles molecular and cellular representations into shared and private components, enabling phenotype-guided alignment while preserving chemical structures through a dedicated molecular branch. This design integrates cellular phenotype information without disrupting molecular neighborhood organization. Experiments on approximately $3.04 \times 10^{4}$ molecule--cell morphology pairs demonstrate that PhenMol improves molecular property prediction across 270 bioactivity tasks, molecule--phenotype retrieval, and clinical trial outcome prediction. Moreover, ECFP4-based structural analysis shows that PhenMol better preserves molecular neighborhoods and reduces embedding distortion compared with existing multimodal alignment methods. These results highlight the importance of structure-aware constraints in multimodal molecular representation learning and provide an effective approach for integrating cellular phenotypes with chemical knowledge for drug discovery.