LipidBERT: A Lipid Language Model Pre-trained on METiS de novo Lipid Library
<|reference_start|>LipidBERT: A Lipid Language Model Pre-trained on METiS de novo Lipid Library: In this study, we generate and maintain a database of 10 million virtual lipids through METiS's in-house de novo lipid generation algorithms and lipid virtual screening techniques. These virtual lipids serve as a corpus for pre-training, lipid representation learning, and downstream task knowledge transfer, culminating in state-of-the-art LNP property prediction performance. We propose LipidBERT, a BERT-like model pre-trained with the Masked Language Model (MLM) and various secondary tasks. Additionally, we compare the performance of embeddings generated by LipidBERT and PhatGPT, our GPT-like lipid generation model, on downstream tasks. The proposed bilingual LipidBERT model operates in two languages: the language of ionizable lipid pre-training, using in-house dry-lab lipid structures, and the language of LNP fine-tuning, utilizing in-house LNP wet-lab data. This dual capability positions LipidBERT as a key AI-based filter for future screening tasks, including new versions of METiS de novo lipid libraries and, more importantly, candidates for in vivo testing for orgran-targeting LNPs. To the best of our knowledge, this is the first successful demonstration of the capability of a pre-trained language model on virtual lipids and its effectiveness in downstream tasks using web-lab data. This work showcases the clever utilization of METiS's in-house de novo lipid library as well as the power of dry-wet lab integration.<|reference_end|>
@article{yu2024lipidbert:,
    title={LipidBERT: A Lipid Language Model Pre-trained on METiS de novo Lipid
  Library},
    author={Tianhao Yu, Cai Yao, Zhuorui Sun, Feng Shi, Lin Zhang, Kangjie Lyu,
  Xuan Bai, Andong Liu, Xicheng Zhang, Jiali Zou, Wenshou Wang, Chris Lai and
  Kai Wang},
    journal={arXiv preprint arXiv:2408.06150},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06150},
    primaryClass={cs.CL physics.chem-ph q-bio.BM}
}