DeepSynergy: predicting anti-cancer drug synergy with Deep Learning

By Kristina Preuer, Richard P. I. Lewis, Sepp Hochreiter, Andreas Bender, Krishna Chaitanya Bulusu, Günter Klambauer. 2017

577 citations recorded by the source corpus.

Abstract Motivation While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a time- and cost-efficient way to prioritize combinations to test, based on recently available large-scale combination screening data. Recently, Deep Learning has had an impact in many research areas by achieving new state-of-the-art model performance. However, Deep Learning has not yet been applied to drug synergy prediction, which is the approach we present here, termed DeepSynergy. DeepSynergy uses chemical and genomic information as input information, a normalization strategy to account for input data heterogeneity, and conical layers to model drug synergies. Results DeepSynergy was compared to other machine learning methods such as Gradient Boosting Machines, Random Forests, Support Vector Machines and Elastic Nets on the largest publicly available synergy dataset with respect to mean squared error. DeepSynergy significantly outperformed the other methods with an improvement of 7.2% over the second best method at the prediction of novel drug combinations within the space of explored drugs and cell lines. At this task, the mean Pearson correlation coefficient between the measured and the predicted values of DeepSynergy was 0.73. Applying DeepSynergy for classification of these novel drug combinations resulted in a high predictive performance of an AUC of 0.90. Furthermore, we found that all compared methods exhibit low predictive performance when extrapolating to unexplored drugs or cell lines, which we suggest is due to limitations in the size and diversity of the dataset. We envision that DeepSynergy could be a valuable tool for selecting novel synergistic drug combinations. Availability and implementation DeepSynergy is available via www.bioinf.jku.at/software/DeepSynergy. Supplementary information Supplementary data are available at Bioinformatics online.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Learning Explainable Models Using Attribution Priors (2019, background): “rattributionpriors, other attribution methods can also be used. In particular, by viewing input gradients as explanations, attribution priors are a generalization of existing gradient-based penalties [16, 26, 40, 14, 27]. 2 To demonstrate how attribution priors can capture human intuition in a varie”
Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018, background): “om Forest classifiers. In addition, DeepDTI showed potential to predict whether a new drug targets to some existed targets, or whether a new target is interacting with some existed drugs. DeepSynergy [121] https://github.com/Kristina Preuer/DeepSynergy Predicts synergistic drug combinations for canc”
Deep learning in cancer diagnosis, prognosis and treatment selection (2021, unknown): “MLP DeepSynergy [75] Cell line gene expression and chemical features of drugs in drug combinations used as input; Predicts ‘synergy score’ between the drug combinations and transcriptomic profiles”
Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells (2020, methodology): “Drug combination proﬁling data across a diverse cell line panel were obtained from DeepSynergy (http://www.bioinf.jku.at/software/ DeepSynergy/) (Preuer et al., 2018), which used the Loewe model of additivity (Loewe, 1953) to evaluate the interaction of 583 different drug combinations across 39 huma”
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, background): “[111] Kristina Preuer, Richard PI Lewis, Sepp Hochreiter, Andreas Bender, Krishna C Bulusu, and 572 Günter Klambauer.”
Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, background): “…characteristics features of drug compounds such as SMILES2Vec (predicts chemical properties) [339], Chemception (DNN-based prediction of chemical properties) [245], DeepSynergy (prediction of anti-cancer drug synergy with DL) [340], and deepAOT (prediction of compound acute oral toxicity) [341].”
Machine learning approaches to drug response prediction: challenges and recent progress (2020, background): “This is done by Preuer et al. 76 where the authors only leverage transcriptomic data and drug structure data to predict Loewe score which quanti ﬁ es the excess over the expected response if the two drugs used in a combination were the same compound.”