Beyond Textual Data: Predicting Drug-Drug Interactions from Molecular Structure Images using Siamese Neural Networks

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

[2]  Jimeng Sun,et al.  CASTER: Predicting Drug Interactions with Chemical Substructure Representation , 2019, AAAI.

[3]  Luca Toldo,et al.  Extraction of potential adverse drug events from medical case reports , 2012, Journal of biomedical semantics.

[4]  Andrew Zisserman,et al.  Spatial Transformer Networks , 2015, NIPS.

[5]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[6]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[7]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

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

[9]  Max Welling,et al.  Transformation Properties of Learned Visual Representations , 2014, ICLR.

[10]  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.

[11]  Xiaofeng He,et al.  Medical Question Retrieval Based on Siamese Neural Network and Transfer Learning Method , 2019, DASFAA Workshops.

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

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

[14]  Makoto Miwa,et al.  Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information , 2018, ACL.

[15]  Kristian Kersting,et al.  Learning Markov Logic Networks via Functional Gradient Boosting , 2011, 2011 IEEE 11th International Conference on Data Mining.

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

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

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

[19]  Pawan Goyal,et al.  Drug-Drug Interactions Prediction Based on Drug Embedding and Graph Auto-Encoder , 2019, 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE).

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

[21]  Jenny Benois-Pineau,et al.  Classification of sMRI for Alzheimer's disease Diagnosis with CNN: Single Siamese Networks with 2D+? Approach and Fusion on ADNI , 2017, ICMR.

[22]  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.

[23]  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.

[24]  Kristian Kersting,et al.  Gradient-based boosting for statistical relational learning: The relational dependency network case , 2011, Machine Learning.

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

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

[27]  Timothy Dozat,et al.  Incorporating Nesterov Momentum into Adam , 2016 .

[28]  Jae Yong Ryu,et al.  Deep learning improves prediction of drug–drug and drug–food interactions , 2018, Proceedings of the National Academy of Sciences.

[29]  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).

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

[31]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[32]  Edward J. Delp,et al.  A Two Stream Siamese Convolutional Neural Network for Person Re-identification , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

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

[34]  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).

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

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

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

[38]  Ji Wu,et al.  Drug-drug Interaction Prediction with Graph Representation Learning , 2019, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[39]  M. Kramer.  Nonlinear principal component analysis using autoassociative neural networks , 1991 .

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

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

[42]  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.

[43]  Alán Aspuru-Guzik,et al.  Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.

[44]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.

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