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<p>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 &gt;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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月23日

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  • Kei Terayama,
  • Shoichi Ishida,
  • Sourav Mete,
  • Kensuke Suga,
  • Ryo Tamura,
  • Shohei Saito,
  • Takashi Nakanishi,
  • Masanobu Naito,
  • Koji Tsuda,
  • Masato Sumita

摘要

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.