Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties.

By Feixiong Cheng, Zhongming Zhao. 2014

333 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Marinka Zitnik
2. Jure Leskovec
3. Stephen H. Bryant
4. Cao Xiao
5. Fei Wang
6. Tengfei Ma
7. Jiayu Zhou
8. Olivier Elemento

In Molecular Biology

1. Marinka Zitnik
2. Jure Leskovec
3. Cao Xiao
4. Tengfei Ma
5. Stephen H. Bryant
6. Fei Wang
7. Yanli Wang
8. Tiejun Cheng

In Pharmacology

1. Stephen H. Bryant
2. Marinka Zitnik
3. Cao Xiao
4. Tengfei Ma
5. Jure Leskovec
6. Jiayu Zhou
7. Sang Yup Lee
8. Fei Wang

Citations in the corpus

• Interpreting Classifiers through Attribute Interactions in Datasets (2017, background): “Two difficult problems involving interactions concern drug-drug interactions in pharmacovigilance (e.g., Zhang et al., 2017; Cheng & Zhao, 2014) and investigating interactions between single nucleotide polymorphisms (SNPs) in bioinformatics (e.g., Lunetta et al., 2004; Moore et al., 2010).”
• Modeling polypharmacy side effects with graph convolutional networks (2018, methodology): “Classification-based methods consider drug-drug interaction prediction as a binary classification problem (Cheng and Zhao, 2014; Huang et al., 2014a; Zitnik and Zupan, 2016; Chen et al., 2016b; Shi et al., 2017).”
• Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders (2018, methodology): “In literature, drug similarity has been computed using molecular structure data [Jin et al. , 2017], interaction profile data [Fokoue et al. , 2016], as well as side-effect information [Cheng and Zhao, 2014; Liu et al. , 2012].”
• Predicting drug–drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge (2017, methodology): “DDI prediction using machine learning approaches was implemented on DDInetworks through integrated phenotypic, therapeutic, structural and genomic similarities [13].”
• Graph convolutional networks for computational drug development and discovery (2019, methodology): “Traditional computational methods aggregate drug and target features or similarity measures to identify interactions using standard machine learning algorithms [59–62].”
• Deep learning improves prediction of drug–drug and drug–food interactions (2018, methodology): “These applications suggest that DeepDDI provides more specific information on drug interactions beyond the occurrence chance of DDIs or ADEs typically reported to date (7).”
• Systems biology approaches for advancing the discovery of effective drug combinations (2015, methodology): “As an example, Cheng and Zhao developed a comprehensive drug-drug interaction network incorporating 6946 interactions of 721 approved drugs using data from DrugBank [66].”
• Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data (2017, methodology): “Cheng et al. [25] applied five predictive models (naive Bayes, decision tree, k -near- est neighbor, logistic regression, and support vector machine) to build prediction models.”