Drug-drug interaction through molecular structure similarity analysis

By Santiago Vilar, R. Harpaz, E. Uriarte, L. Santana, Raul Rabadan, Carol Friedman. 2012

260 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Toxicology

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. Fei Wang
3. Jure Leskovec
4. Stephen H. Bryant
5. Anna Goldenberg
6. Jiayu Zhou
7. Olivier Elemento
8. Sang Yup Lee

In Molecular Biology

1. Marinka Zitnik
2. Fei Wang
3. Jure Leskovec
4. Anna Goldenberg
5. Stephen H. Bryant
6. Michael M. Hoffman
7. Francis Nguyen
8. Yanli Wang

In Toxicology

1. Fei Wang
2. Jure Leskovec
3. Marinka Zitnik

Citations in the corpus

• Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. (2014, unknown): “Equation 3 has been corrected.”
• Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, background): “In contrast, similarity-based approaches assume that similar drugs have similar interaction patterns [298, 299, 289, 254, 33, 291, 300].”
• Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects (2015, methodology): “Similarity-based approach is a representative strategy for DDI prediction 10,12,15 .”
• Deep learning improves prediction of drug–drug and drug–food interactions (2018, background): “This inference was based on a well-known assumption that a drug pair A is likely to have the same DDI as another drug pair B if both drug pairs have structurally similar drug members (8).”
• Towards Personalized Medicine: Leveraging Patient Similarity and Drug Similarity Analytics (2014, background): “Drug similarity analytics has been widely used in drug repositioning 7-9 , drug side-effects prediction 10 , drug-target interactions prediction 11 , and drug-drug interactions prediction 12, 13 applications.”
• 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].”
• Predicting drug–drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge (2017, methodology): “Structural similarity for DDI prediction has been employed based on the idea that if there is a DDI between drug A and drug B, and drug C has a similar structure to drug A, there is likely a DDI between drug C and drug B [8].”