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
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.
Jiayi Zhang, Yujie Chen, Zhou Yu et al.· Journal of Chemical Informat...· 0 citations
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