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arXiv Machine Learning By Acer Blake, Fergus Imrie, Charlotte M. Deane

On the limitations of molecular representations

Read the original on arXiv Machine Learning →

The paper evaluates molecular representations used in cheminformatics and small‑molecule machine learning, such as fingerprints and learned embeddings. It finds that current state‑of‑the‑art embedding models do not outperform traditional fingerprints or physicochemical descriptors in capturing graph structure, 3D shape, or ADMET similarity. Additionally, fingerprints exhibit significant limitations, with 225 million to 4 billion molecules in the 10.8 billion‑molecule Enamine REAL database sharing non‑unique representations, which impacts virtual screening.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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