LipidBERT: A Lipid Language Model Pre-trained on METiS de novo Lipid Library
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
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2024
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| _version_ | 1866915271970652160 |
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| author | Yu, Tianhao Yao, Cai Sun, Zhuorui Shi, Feng Zhang, Lin Lyu, Kangjie Bai, Xuan Liu, Andong Zhang, Xicheng Zou, Jiali Wang, Wenshou Lai, Chris Wang, Kai |
| author_facet | Yu, Tianhao Yao, Cai Sun, Zhuorui Shi, Feng Zhang, Lin Lyu, Kangjie Bai, Xuan Liu, Andong Zhang, Xicheng Zou, Jiali Wang, Wenshou Lai, Chris Wang, Kai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06150 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LipidBERT: A Lipid Language Model Pre-trained on METiS de novo Lipid Library Yu, Tianhao Yao, Cai Sun, Zhuorui Shi, Feng Zhang, Lin Lyu, Kangjie Bai, Xuan Liu, Andong Zhang, Xicheng Zou, Jiali Wang, Wenshou Lai, Chris Wang, Kai Computation and Language Chemical Physics Biomolecules 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. |
| title | LipidBERT: A Lipid Language Model Pre-trained on METiS de novo Lipid Library |
| topic | Computation and Language Chemical Physics Biomolecules |
| url | https://arxiv.org/abs/2408.06150 |