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Quantifying the applicability domain between two datasets using Tanimoto similarity

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Machine learning is a popular technique used in predictive toxicology, where predicting the activity of compounds on targets/endpoints is an important aspect. During the process of machine learning, training and test datasets need to be used where the applicability domain between these two datasets would give an indication of the performance of the machine learning model, as well as whether the model can be applied to other datasets. In this work, a method that uses Tanimoto similarity on molecular fingerprints is described that quantifies the applicability domain between two datasets. A total of 76 human targets, of which the data is publicly available was tested and the results obtained generally show low absolute error rates (

This talk is part of the Theory - Chemistry Research Interest Group series.

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