MolAtlas: a visualization framework for molecular property distributions to guide functional molecule development

Abstract Recent advances in generative AI have sparked anticipation of a future where functional molecules that potentially outperform existing ones can be freely designed. However, as a comprehensive understanding of the property space defined by known functional molecules is lacking, assessing whether new AI/human-designed molecules truly surpass existing ones is challenging. To address this, we computed ~50 experimentally observable properties for >5 million molecules curated from three datasets covering commercially available, reported, and artificially constructed compounds using density functional theory. Based on them, we developed MolAtlas, a visualization system that reveals statistical boundaries and property relationships within an observable property space, offering a reference framework for evaluating molecular novelty. As an example, by analyzing frontier orbital energies, we proposed an empirical requirement for molecular air-stability. We further demonstrated the utility of the system by identifying a small fluorescent compound and characterizing charge-retaining molecules for liquid electrets. Thus, MolAtlas provides a data-driven foundation for navigating the molecular property space and accelerating functional molecule design.