Similarity maps - a visualization strategy for molecular fingerprints and machine-learning methods
Open access
Date
2013-09Type
- Journal Article
ETH Bibliography
no
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Abstract
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 naïve Bayes. An open-source implementation of the method is provided. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000088799Publication status
publishedExternal links
Journal / series
Journal of CheminformaticsVolume
Pages / Article No.
Publisher
Chemistry CentralSubject
Visualization; Machine-learning; Similarity; FingerprintsOrganisational unit
09458 - Riniker, Sereina Z. / Riniker, Sereina Z.
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ETH Bibliography
no
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