Combination therapy is widely used to treat complex diseases, yet predicting synergistic drug combinations remains challenging for rare and underrepresented cell lines due to severe data sparsity. Existing methods rely primarily on observed drug–cell responses and learn representations within a limited experimental knowledge regime, resulting in poor generalization in cold-start and long-tail settings. To address this limitation, we propose L2aCo, a model-agnostic knowledge alignment framework that leverages large language models as external biomedical knowledge sources to expand the effective knowledge boundary for drug combinations. L2aCo augments drug and cell-line representations with LLM-inferred semantic profiles capturing biological mechanisms and contextual relations beyond experimental measurements. These semantic representations are integrated with conventional molecular and cellular features through a representation-level alignment mechanism, which injects relational knowledge while preserving task-relevant experimental signals. By enabling effective knowledge transfer under data scarcity, L2aCo substantially improves generalization for cold-start and long-tail cell lines. Experiments on multiple public benchmarks show consistent and significant performance gains for novel cell lines when L2aCo is integrated with drug synergy predictors, with pronounced improvements on novel cell lines.
Tengfei Ma, Yuqin He, Zhonghao Ren et al.· Proceedings of the 32nd ACM...· 0 citations
This work proposes 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.
Yujie Chen, Tengfei Ma, Zhou Yu et al.· Proceedings of the 32nd ACM...· 0 citations
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.
Yujie Chen, Tengfei Ma, Zhou Yu et al.· Proceedings of the 32nd ACM...· 0 citations
This work proposes GuideRetro, a synthesizability-aware framework for multi-step retrosynthetic planning that integrates global syn-thesizability knowledge into step-wise retrosyn-thetic prediction and improves planning accuracy and search efficiency under realistic retrosynthetic settings.
Yujie Chen, Ajie Lin, Tengfei Ma et al.· 0 citations
PhenMol disentangles molecular and cellular representations into shared and private components, enabling phenotype-guided alignment while preserving chemical structures through a dedicated molecular branch, and improves molecular property prediction across 270 bioactivity tasks, molecule--phenotype retrieval, and clinical trial outcome prediction.
Xuan Lin, Jingyu Sheng, Tengfei Ma et al.· 0 citations
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