SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines
By Tong He, Marten Heidemeyer, Fuqiang Ban, Artem Cherkasov, Martin Ester. Journal of Cheminformatics, 2017
463 citations recorded by the source corpus.
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.
Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/ec0e3bd70a6ae141804c2fba85e2b25ee1311def
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Citations in the corpus
- DeepPurpose: a deep learning library for drug–target interaction prediction (2020, unknown): “, 2011] and KIBA [He et al., 2017] (Figure 1D).”
- Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles (2020, methodology): “In addition, XGB has excelled at predicting drug–target binding affinities [25], where it outperformed widely used regularized least-squares techniques on the studied datasets.”
- Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model (2020, unknown): “Neverthless, MT-DTI showed the best performance [12] when compared to a deep learning-based (DeepDTA) approach [21] and two traditional machine learning-based algorithms SimBoost [22], and KronRLS [23], with the KIBA [24] and DAVIS [25] data sets.”