L2aCo is 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 and substantially improves generalization for cold-start and long-tail cell lines.
Tengfei Ma, Yuqin He, Zhong-Hao 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.
Yu-Jie 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 unl...
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.
Yu-Jie Chen, Ajie Lin, Tengfei Ma et al.· Proceedings of the Thirty-Fi...· 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 clini...
Xuan Lin, Jingyu Sheng, Tengfei Ma et al.· 0 citations
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