Mol-Instructions can be leveraged to evaluate cross-modal comprehension in general models by training these models to interpret user intentions and decode biomolecular language. By exposing the models to a diverse range of biomolecular instructions encompassing molecule-oriented, protein-oriented, and biomolecular text tasks, they can develop a deeper understanding of the intricate language used in the field of biomolecules. The dataset provides a structured framework for instructing LLMs on how to analyze and predict molecular properties, understand protein functions, design proteins based on textual directives, and extract information from bioinformatics texts.
To assess cross-modal comprehension using Mol-Instructions, researchers can design experiments where LLMs are tasked with interpreting complex biomolecular instructions across different modalities such as molecules, proteins, and bioinformatics texts. By evaluating the model's performance on these tasks against baseline metrics or specialized smaller models trained specifically for each domain within biomedicine research, researchers can gauge the model's ability to comprehend and generate accurate outputs across various aspects of biomolecular studies.