Empowering Molecule Discovery for Molecule-Caption Translation With Large Language Models: A ChatGPT Perspective

作者：Jiatong Li, Yunqing Liu, Wenqi Fan, Xiaoyong Wei, Hui Liu, Jiliang Tang, Qing Li · 发表于：IEEE Transactions on Knowledge and Data Engineering · 年份：2024 · DOI：10.1109/tkde.2024.3393356 · 被引用次数：64 · 研究领域：Topic Modeling、Natural Language Processing Techniques、Machine Learning in Materials Science

Molecule discovery plays a crucial role in various scientific fields, advancing the design of tailored materials and drugs, which contributes to the development of society and human well-being. Specifically, molecule-caption translation is an important task for molecule discovery, aligning human understanding with molecular space. However, most of the existing methods heavily rely on domain experts, require excessive computational cost, or suffer from sub-optimal performance. On the other hand, Large Language Models (LLMs), like ChatGPT, have shown remarkable performance in various cross-modal tasks due to their powerful capabilities in natural language understanding, generalization, and in-context learning (ICL), which provides unprecedented opportunities to advance molecule discovery. Despite several previous works trying to apply LLMs in this task, the lack of domain-specific corpus and difficulties in training specialized LLMs still remain challenges. In this work, we propose a novel LLM-based framework (MolReGPT) for molecule-caption translation, where an In-Context Few-Shot Molecule Learning paradigm is introduced to empower molecule discovery with LLMs like ChatGPT to perform their in-context learning capability without domain-specific pre-training and fine-tuning. MolReGPT leverages the principle of molecular similarity to retrieve similar molecules and their text descriptions from a local database to enable LLMs to learn the task knowledge from context examples. We e...