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.
Highlights
Chemical structures are often represented by molecular fingerprints where structural features are converted to either bits in a bit vector or counts in a count vector
The “atomic weights” are generated by removing the bits belonging to the corresponding atom and comparing the resulting similarity with the similarity of the unmodified fingerprint
Similarity maps can be generated for every fingerprint that allows a backtracking of the bits to a corresponding atom or substructure
Summary
Chemical structures are often represented by molecular fingerprints where structural features are converted to either bits in a bit vector or counts in a count vector. This abstract representation allows the computationally efficient handling and comparison of chemical structures. Depending on the descriptors used to generate the fingerprints, the interpretation of the resulting similarity may not be trivial This problem worsens when machine-learning (ML) models are trained to predict the activity (or other properties) of new compounds: ML models often appear as complete “black boxes” that just output numeric predictions to their users. Though these predictions can be quite accurate, it has been shown that supplementing numeric predictions with additional information from the model can improve the ability of both expert and non-expert users to work
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