Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
This addresses a problem for researchers in biomolecular studies by providing a specialized dataset to improve AI capabilities, though it is incremental as it builds on existing instruction tuning methods.
The authors tackled the limited proficiency of large language models in biomolecular studies by introducing Mol-Instructions, a comprehensive instruction dataset for this domain, and demonstrated its effectiveness in enhancing model performance through instruction tuning experiments.
Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields. However, their proficiency within specialized domains such as biomolecular studies remains limited. To address this challenge, we introduce Mol-Instructions, a comprehensive instruction dataset designed for the biomolecular domain. Mol-Instructions encompasses three key components: molecule-oriented instructions, protein-oriented instructions, and biomolecular text instructions. Each component aims to improve the understanding and prediction capabilities of LLMs concerning biomolecular features and behaviors. Through extensive instruction tuning experiments on LLMs, we demonstrate the effectiveness of Mol-Instructions in enhancing large models' performance in the intricate realm of biomolecular studies, thus fostering progress in the biomolecular research community. Mol-Instructions is publicly available for ongoing research and will undergo regular updates to enhance its applicability.