Predicting Drug-Drug Interactions from Molecular Structure Images

Predicting and discovering drug-drug interactions (DDIs) is an important problem and has been studied extensively both from medical and machine learning point of view. Almost all of the machine learning approaches have focused on text data or textual representation of the structural data of drugs. We present the first work that uses drug structure images as the input and utilizes a Siamese convolutional network architecture to predict DDIs.

[1]  C. Richards,et al.  Emergency hospitalizations for adverse drug events in older Americans. , 2011, The New England journal of medicine.

[2]  Yann LeCun,et al.  Signature Verification Using A "Siamese" Time Delay Neural Network , 1993, Int. J. Pattern Recognit. Artif. Intell..

[3]  D. Bates,et al.  Incidence and preventability of adverse drug events among older persons in the ambulatory setting. , 2003, JAMA.

[4]  A. Y. Lu,et al.  Human cytochrome P-450 3A4: in vitro drug-drug interaction patterns are substrate-dependent. , 2000, Drug metabolism and disposition: the biological fate of chemicals.

[5]  P. Barach,et al.  Clarifying Adverse Drug Events: A Clinician's Guide to Terminology, Documentation, and Reporting , 2004, Annals of Internal Medicine.

[6]  Renée J. G. Arnold,et al.  Impact of Definitive Drug–Drug Interaction Testing on Medication Management and Patient Care , 2018, Drugs - Real World Outcomes.

[7]  J S Roberts,et al.  Quantifying the Clinical Significance of Drug—Drug Interactions: Scaling Pharmacists' Perceptions of a Common Interaction Classification Scheme , 1996, The Annals of pharmacotherapy.

[8]  Zhongming Zhao,et al.  Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. , 2014, Journal of the American Medical Informatics Association : JAMIA.

[9]  Luca Bertinetto,et al.  Fully-Convolutional Siamese Networks for Object Tracking , 2016, ECCV Workshops.

[10]  V. Matousek,et al.  Signature verification using ART-2 neural network , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[11]  Gang Wang,et al.  Gated Siamese Convolutional Neural Network Architecture for Human Re-identification , 2016, ECCV.

[12]  C. Pham-Huy,et al.  Chiral Drugs: An Overview , 2006, International journal of biomedical science : IJBS.

[13]  D. Bates,et al.  Adverse drug events occurring following hospital discharge , 2005, Journal of General Internal Medicine.

[14]  Deepak Padmanabhan,et al.  A review of drug isomerism and its significance , 2013, International journal of applied & basic medical research.

[15]  Gregory R. Koch,et al.  Siamese Neural Networks for One-Shot Image Recognition , 2015 .

[16]  Hsinchun Chen,et al.  AZDrugMiner: An Information Extraction System for Mining Patient-Reported Adverse Drug Events in Online Patient Forums , 2013, ICSH.

[17]  Zhenghua Yu,et al.  On the Earth Mover's Distance as a histogram similarity metric for image retrieval , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[18]  Paul B. Watkins,et al.  Atorvastatin Reduces the Ability of Clopidogrel to Inhibit Platelet Aggregation: A New Drug–Drug Interaction , 2003, Circulation.

[19]  David Weininger,et al.  SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..

[20]  Alberto Lavelli,et al.  FBK-irst : A Multi-Phase Kernel Based Approach for Drug-Drug Interaction Detection and Classification that Exploits Linguistic Information , 2013, *SEMEVAL.

[21]  S. Preskorn,et al.  The economic consequences of a drug-drug interaction. , 2001, Journal of clinical psychopharmacology.

[22]  K. Maeda,et al.  DRUG-DRUG INTERACTION BETWEEN PITAVASTATIN AND VARIOUS DRUGS VIA OATP1B1 , 2006, Drug Metabolism and Disposition.

[23]  Richard B. Berlin,et al.  Predicting adverse drug events from personal health messages. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[24]  Sriraam Natarajan,et al.  Drug-Drug Interaction Discovery: Kernel Learning from Heterogeneous Similarities. , 2018, Smart health.

[25]  Yann LeCun,et al.  Dimensionality Reduction by Learning an Invariant Mapping , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[26]  D. Pollock,et al.  National surveillance of emergency department visits for outpatient adverse drug events. , 2006, JAMA.

[27]  César de Pablo-Sánchez,et al.  Using a shallow linguistic kernel for drug-drug interaction extraction , 2011, J. Biomed. Informatics.

[28]  B. Stricker,et al.  Hospitalisations and emergency department visits due to drug–drug interactions: a literature review , 2007, Pharmacoepidemiology and drug safety.

[29]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2004 .

[30]  Yann LeCun,et al.  Learning a similarity metric discriminatively, with application to face verification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[31]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.