Foundation models (FMs) have exhibited remarkable performance across a wide range of downstream tasks in many domains. Nevertheless, general-purpose FMs often face challenges when confronted with domain-specific problems, due to their limited access to the proprietary training data in a particular domain. In biomedicine, there are various biological modalities, such as molecules, proteins, and cells, which are encoded by the language of life and exhibit significant modality gaps with human natural language. In this paper, we introduce BioMedGPT, an open multimodal generative pre-trained transformer (GPT) for biomedicine, to bridge the gap between the language of life and human natural language. BioMedGPT allows users to easily ``communicate'' with diverse biological modalities through free text, which is the first of its kind. BioMedGPT aligns different biological modalities with natural language via a large generative language model, namely, BioMedGPT-LM. We publish BioMedGPT-10B, which unifies the feature spaces of molecules, proteins, and natural language via encoding and alignment. Through fine-tuning, BioMedGPT-10B outperforms or is on par with human and significantly larger general-purpose foundation models on the biomedical QA task. It also demonstrates promising performance in the molecule QA and protein QA tasks, which could greatly accelerate the discovery of new drugs and therapeutic targets. In addition, BioMedGPT-LM-7B is the first large generative language model based on Llama2 in the biomedical domain, therefore is commercial friendly. Both BioMedGPT-10B and BioMedGPT-LM-7B are open-sourced to the research community. In addition, we publish the datasets that are meticulously curated for the alignment of multi-modalities, i.e., PubChemQA and UniProtQA. All the models, codes, and datasets are available at \url{https://github.com/PharMolix/OpenBioMed}.

翻译：基础模型已在多个领域的众多下游任务中展现出卓越性能。然而，通用型基础模型在面对特定领域问题时往往面临挑战，原因在于其难以获取特定领域中的专有训练数据。在生物医学领域，存在多种生物模态，如分子、蛋白质和细胞，这些模态由生命语言编码，并与人类自然语言存在显著的模态鸿沟。本文介绍了BioMedGPT——一种面向生物医学的开放多模态生成式预训练Transformer，旨在弥合生命语言与人类自然语言之间的鸿沟。BioMedGPT使用户能够通过自由文本轻松与多种生物模态进行"交流"，这在该领域尚属首次。BioMedGPT通过大型生成式语言模型（即BioMedGPT-LM）将不同生物模态与自然语言对齐。我们发布了BioMedGPT-10B，该模型通过编码与对齐统一了分子、蛋白质和自然语言的特征空间。经微调后，BioMedGPT-10B在生物医学问答任务上优于或与人类及显著更大的通用型基础模型表现相当。在分子问答和蛋白质问答任务中，该模型也展现出令人期待的性能，有望极大加速新药与治疗靶点的发现。此外，BioMedGPT-LM-7B是生物医学领域首个基于Llama2的大型生成式语言模型，因此具有商用友好性。BioMedGPT-10B和BioMedGPT-LM-7B均已向研究社区开源。同时，我们还发布了为多模态对齐精心整理的数据集——PubChemQA和UniProtQA。所有模型、代码及数据集均可在\url{https://github.com/PharMolix/OpenBioMed}获取。