ArticleMolecular informatics2026
MSI: A Mahalanobis-Based Molecular Similarity Index for High-Dimensional Embeddings.
Article in Molecular informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Quantifying molecular similarity is crucial for drug discovery and for exploring chemical space. A similarity assessment always combines two independent ingredients: a molecular representation and a similarity coefficient. The most common pairing, binary substructure fingerprints scored with the Tanimoto coefficient, depends strongly on fingerprint bit density and, because it compares unweighted sets of substructure identifiers, is blind to the multiplicity of repeated fragments and frequently returns ranking ties that obscure meaningful chemical relationships. Here, we introduce the Mahalanobis Similarity Index (MSI), which pairs continuous Mol2Vec embeddings with a covariance-aware Mahalanobis distance (d
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