SimBoost

Computational prediction of the interaction between drugs and targets is a
standing challenge in the field of drug discovery. A number of rather accurate
predictions were reported for various binary drug-target benchmark datasets.
However, a notable drawback of a binary representation of interaction data is
that missing endpoints for non-interacting drug-target pairs are not
differentiated from inactive cases, and that predicted levels of activity depend
on pre-defined binarization thresholds. In this paper, we present a method called
SimBoost that predicts continuous (non-binary) values of binding affinities of
compounds and proteins and thus incorporates the whole interaction spectrum from
true negative to true positive interactions. Additionally, we propose a version
of the method called SimBoostQuant which computes a prediction interval in order
to assess the confidence of the predicted affinity, thus defining the
Applicability Domain metrics explicitly. We evaluate SimBoost and SimBoostQuant
on two established drug-target interaction benchmark datasets and one new dataset
that we propose to use as a benchmark for read-across cheminformatics
applications. We demonstrate that our methods outperform the previously reported
models across the studied datasets.

SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines.

SimBoost is a software tool to predict continuous values of binding affinities of compounds and proteins.

SimBoost

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