Similarity maps…

Similarity maps – a visualization strategy for molecular fingerprints and machine-learning methods by Sereina Riniker and Gregory A Landrum.


Fingerprint similarity is a common method for comparing chemical structures. Similarity is an appealing approach because, with many fingerprint types, it provides intuitive results: a chemist looking at two molecules can understand why they have been determined to be similar. This transparency is partially lost with the fuzzier similarity methods that are often used for scaffold hopping and tends to vanish completely when molecular fingerprints are used as inputs to machine-learning (ML) models. Here we present similarity maps, a straightforward and general strategy to visualize the atomic contributions to the similarity between two molecules or the predicted probability of a ML model. We show the application of similarity maps to a set of dopamine D3 receptor ligands using atom-pair and circular fingerprints as well as two popular ML methods: random forests and na?ve Bayes. An open-source implementation of the method is provided.

If you are doing topic maps in areas where molecular fingerprints are relevant, this could be quite useful.

Despite my usual warnings that semantics are continuous versus the discrete structures treated here, this may also be useful in “fuzzier” areas where topic maps are found.

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