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 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 task, LiCore achieves near-perfect reconstruction performance 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 predictor, LiGen generates novel lipid candidates that achieve a 30.7% relative improvement over baseline methods in predicted delivery efficiency, with some candidates exceeding 50% improvement.