BioMedGPT: An Open Multimodal Large Language Model for BioMedicine

Recent advances in large language models (LLMs) like ChatGPT have shed light on the development of knowledgeable and versatile AI research assistants in various scientific domains. However, they fall short in biomedical applications due to a lack of proprietary biomedical knowledge and deficiencies in handling biological sequences for molecules and proteins. To address these issues, we present BioMedGPT, a multimodal large language model for assisting biomedical research. We first incorporate domain expertise into LLMs by incremental pre-training on large-scale biomedical literature. Then, we harmonize 2D molecular graphs, protein sequences, and natural language within a unified, parameter-efficient fusion architecture by fine-tuning on multimodal question-answering datasets. Through comprehensive experiments, we show that BioMedGPT performs on par with human experts in comprehending biomedical documents and answering research questions. It also exhibits promising capability in analyzing intricate functions and properties of novel molecules and proteins, surpassing state-of-the-art LLMs by 17.1% and 49.8% absolute gains respectively in ROUGE-L on molecule and protein question-answering.