Projects per year
Abstract
It is common in cheminformatics to represent the properties of a ligand as a string of 1’s and 0’s, with the intention of elucidating, inter alia, the relationship between the chemical structure of a ligand and its bioactivity. In this commentary we note that, where relevant but non-redundant features are binary, they inevitably lead to a classifier capable of capturing only a linear relationship between structural features and activity. If, instead, we were to use relevant but non-redundant real-valued features, the resulting predictive model would be capable of describing a non-linear structure-activity relationship. Hence, we suggest that real-valued features, where available, are to be preferred in this scenario.
Original language | English |
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Article number | 58 |
Journal | Journal of Cheminformatics |
Volume | 7 |
DOIs | |
Publication status | Published - 1 Dec 2015 |
Keywords
- Binary descriptors
- Ligand chemical structure
- Linear relationship
- Bernoulli distribution
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Dive into the research topics of 'A note on utilising binary features as ligand descriptors'. Together they form a unique fingerprint.Projects
- 1 Finished
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Machine Learning Approaches to Predict: Machine Learning Approaches to Predict Enzyme Function
Mitchell, J. B. O. (PI) & De Ferrari, L. (Researcher)
1/09/11 → 31/12/14
Project: Standard