Lipid nanoparticles (LNPs) can deliver cargos to both tumor and immune cells, playing a crucial role in biomedicine. Traditional approaches rely on experimental screening and expert knowledge, which can be costly and time-consuming. Recent methods based on language models have accelerated this process using deep learning. Although these methods can retrieve molecules for fusion or rank candidates from existing libraries, they are still limited by the scope of known formulations. In this work, we propose a method, LiGen, to generate lipid molecules efficiently and actively, facilitating the discovery of high-performing LNP formulations. We first train a lipid-specific molecular language model, LiCore, to learn hidden representations of lipid molecules. We then explore the learned latent space to generate improved candidate formulations. This process is guided by a trained predictor, which evaluates delivery efficiency and provides directional signals. In reconstruction tasks, LiCore achieves nearly perfect reconstruction output with a low invalid ratio on both the LNP-Virtual900k and LNP-Exp12k datasets. The predictor consistently improves ranking-oriented metrics across multiple cell lines, with our method outperforming the best baselines by an average of 4.1%, 10.8%, and 8.1% in Top-50, Top-10, and Top-5 identification accuracy, respectively. Guided by the predictor, LiGen generates novel lipid candidates that achieve a 30.7% improvement over baseline methods on average, with some samples exceeding 50% improvement.

Text-attributed graphs (TAGs) require jointly modeling relational structure and node-level text. Existing GNN-LLM approaches perform by incorporating large language models at inference time for processing the text attributes, resulting in costly deployment. More fundamentally, LLM knowledge is typically used in a sample-wise manner, leading to inefficient utilization across graph instances. In this work, we study how interactions with LLM embedding spaces affect graph representations, and show that projecting into the LLM space can learn better GNNs. That is to say, the knowledge encoded in LLM embeddings can be compressed into graph representations. Based on this insight, we propose a framework that internalizes LLM knowledge within graph models and supports inference-efficient TAG learning. Our framework employs a hierarchical Proxy-Purifier module with distribution-level regularization, using LLM embeddings only as training-time guidance. With this module, the model operates TAGs without invoking LLMs, achieving high efficiency as standard GNNs without LLMs. Notably, experiments on five popular TAG tasks further demonstrate that our method can also achieve consistent performance gains, in comparison to existing GNN-LLM approaches.