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Guy Shtar et al.
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Geonhee Lee et al.
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SFLLN: A sparse feature learning ensemble method with linear neighborhood regularization for predicting drug-drug interactions
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Seung Hwan Lee et al.
EXPERT OPINION ON DRUG METABOLISM & TOXICOLOGY (2019)
DrugBank 5.0: a major update to the DrugBank database for 2018
David S. Wishart et al.
NUCLEIC ACIDS RESEARCH (2018)
Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era (vol 20, pg 58, 2018)
Yankang Jing et al.
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Marinka Zitnik et al.
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Hui Yu et al.
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Andrej Kastrin et al.
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Takako Takeda et al.
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Wen Zhang et al.
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Junshui Ma et al.
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Ping Zhang et al.
SCIENTIFIC REPORTS (2015)
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Feixiong Cheng et al.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2014)
INDI: a computational framework for inferring drug interactions and their associated recommendations
Assaf Gottlieb et al.
MOLECULAR SYSTEMS BIOLOGY (2012)
Extended-Connectivity Fingerprints
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