Enhancing the Effectiveness of Ligand‐Based Virtual Screening Using Data Fusion

Data fusion is being increasingly used to combine the outputs of different types of sensors. This paper reviews the application of the approach to ligand-based virtual screening, where the sensors to be combined are functions that score molecules in a database on their likelihood of exhibiting some required biological activity. Much of the literature to date involves the combination of multiple similarity searches, although there is also an increasing interest in the combination of multiple machine-learning techniques. Both approaches are reviewed here, focusing on the extent to which fusion can improve the effectiveness of searching when compared with a single screening mechanism, and on the reasons that have been suggested for the observed performance enhancement.

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