论分子表示的局限性
On the limitations of molecular representations
Acer Blake, Fergus Imrie, Charlotte M. Deane
AI 速览：系统评估分子表示质量，发现先进嵌入模型在捕捉图结构、3D形状和ADMET端点相似性上并不优于指纹或理化描述符，且经典指纹在大型数据库中亦存在表示非唯一性问题。
Molecular representations such as fingerprints and learned embeddings play a central role in cheminformatics and small-molecule machine learning. They serve as the primary input for a diverse array of tasks, including clustering, dataset splitting, and virtual screening, yet their evaluation is often limited to downstream model performance on narrow benchmark tasks. We carried out a systematic investigation of the quality of molecular representations, finding that none of the state-of-the-art molecular embedding models capture graph structure, 3D shape, or ADMET endpoint similarity better than fingerprints or physicochemical descriptors. However, classical fingerprints also revealed their own material limitations. For example, we show that depending on the representation, between 225M and $\sim$4B molecules have non-unique representations in the 10.8B Enamine REAL database, with implications for virtual screening. Our findings provide an insight into the current limitations of molecular representations, outlining future research directions for the development of more robust encodings of small molecules.