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Predicting adverse drug reactions of two‐drug combinations using structural and transcriptomic drug representations to train an artificial neural network

https://doi.org/10.1111/cbdd.13802
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Incidence of Adverse Drug Reactions in Hospitalized Patients
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CompoundDB4j: Integrated Drug Resource of Heterogeneous Chemical Databases
inferredcites →https://doi.org/10.1002/pds.3309openalex-snapshot
Frequency of adverse drug reactions in hospitalized patients: a systematic review and meta‐analysis
inferredcites →https://doi.org/10.1007/978-3-642-35289-8_25openalex-snapshot
Stochastic Gradient Descent Tricks
inferredcites →https://doi.org/10.1016/j.cell.2017.10.049openalex-snapshot
A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles
inferredcites →https://doi.org/10.1016/s1470-2045(04)01528-1openalex-snapshot
Drug interactions in oncology
inferredcites →https://doi.org/10.1038/s41598-019-56525-5openalex-snapshot
Database of adverse events associated with drugs and drug combinations
inferredcites →https://doi.org/10.1073/pnas.1803294115openalex-snapshot
Deep learning improves prediction of drug–drug and drug–food interactions
inferredcites →https://doi.org/10.1093/bib/bbaa040openalex-snapshot
Prediction of drug adverse events using deep learning in pharmaceutical discovery
inferredcites →https://doi.org/10.1093/bib/bbx044openalex-snapshot
Deep learning for healthcare: review, opportunities and challenges
inferredcites →https://doi.org/10.1093/bioinformatics/btw168openalex-snapshot
Drug-induced adverse events prediction with the LINCS L1000 data
inferredcites →https://doi.org/10.1093/bioinformatics/bty294openalex-snapshot
Modeling polypharmacy side effects with graph convolutional networks
inferredcites →https://doi.org/10.1093/nar/gku1003openalex-snapshot
STRING v10: protein–protein interaction networks, integrated over the tree of life
inferredcites →https://doi.org/10.1093/nar/gkv1075openalex-snapshot
The SIDER database of drugs and side effects
inferredcites →https://doi.org/10.1093/nar/gkx1037openalex-snapshot
DrugBank 5.0: a major update to the DrugBank database for 2018
inferredcites →https://doi.org/10.1097/00132586-199902000-00059openalex-snapshot
Incidence of Adverse Drug Reactions in Hospitalized Patients: A Meta-Analysis of Prospective Studies
inferredcites →https://doi.org/10.1101/843029openalex-snapshot
DeepSide: A Deep Learning Framework for Drug Side Effect Prediction
inferredcites →https://doi.org/10.1108/ir.1999.04926fae.001openalex-snapshot
Lecture Notes in Computer Science 1205
inferredcites →https://doi.org/10.1109/72.623200openalex-snapshot
Asymptotic statistical theory of overtraining and cross-validation
inferredcites →https://doi.org/10.1109/embc.2016.7591228openalex-snapshot
Using constrained information entropy to detect rare adverse drug reactions from medical forums
inferredcites →https://doi.org/10.1111/cbdd.12378openalex-snapshot
Drug Repurposing Based on Drug–Drug Interaction
inferredcites →https://doi.org/10.1126/scitranslmed.3003377openalex-snapshot
Data-Driven Prediction of Drug Effects and Interactions
inferredcites →https://doi.org/10.1136/amiajnl-2013-001612openalex-snapshot
Mining clinical text for signals of adverse drug-drug interactions
inferredcites →https://doi.org/10.1177/2042098619865413openalex-snapshot
Postmarket surveillance: a review on key aspects and measures on the effective functioning in the context of the United Kingdom and Canada
inferredcites →https://doi.org/10.1186/s12859-018-2520-8openalex-snapshot
Predicting adverse drug reactions of combined medication from heterogeneous pharmacologic databases
inferredcites →https://doi.org/10.1186/s40360-017-0153-6openalex-snapshot
Data-driven prediction of adverse drug reactions induced by drug-drug interactions
inferredcites →https://doi.org/10.18535/ijmsci/v5i2.10openalex-snapshot
The study on prevalence of polypharmacy in elderly patients presenting for first time to the geriatric clinic of a tertiary care hospital in Kerala
inferredcites →https://doi.org/10.2174/1568026616666160530182013openalex-snapshot
A Survey on Computational Approaches to Predicting Adverse Drug Reactions
inferredcites →https://doi.org/10.3233/jrs-180025openalex-snapshot
Reporting of adverse drug reactions in India: A review of the current scenario, obstacles and possible solutions
inferredcites →https://doi.org/10.3389/fphar.2019.00265openalex-snapshot
Risk of Drug-Drug Interactions in Out-Hospital Drug Dispensings in France: Results From the DRUG-Drug Interaction Prevalence Study
inferredcites →https://doi.org/10.4467/20838476si.16.004.6185openalex-snapshot
On Loss Functions for Deep Neural Networks in Classification
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.48550/arxiv.1910.05446openalex-snapshot
On Empirical Comparisons of Optimizers for Deep Learning
inferredcites →https://doi.org/10.6084/m9.figshare.12579704openalex-snapshot
2.1 Drug-pair Representation and the Associated ADRs
inferredcites →https://doi.org/10.6084/m9.figshare.12581228openalex-snapshot
Supplementary S3. Comparison of Various Machine Learning Models with ANNs:extraTree_and_Logistic_Regression.tar.gz
inferredcites →https://doi.org/10.6084/m9.figshare.12582032openalex-snapshot
Supplementary S3. Comparison of Various Machine Learning Models with ANNs- KNN_and_Naive_Bayes
inferredcites →https://doi.org/10.6084/m9.figshare.12582818openalex-snapshot
3.2 Performance of the ANN Model
inferredcites →https://doi.org/10.6084/m9.figshare.12589547openalex-snapshot
3.1 Comparison Between the Five Input Datasets Constructed Based on Number of Samples Manifesting a Particular ADR
inferredcites →https://doi.org/10.6084/m9.figshare.12602150openalex-snapshot
PerformanceOfFiveCutOffs
inferredcites →https://doi.org/10.7551/mitpress/4937.001.0001openalex-snapshot
Neural Smithing

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
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DIGEP‐Pred 2.0: A web application for predicting drug‐induced cell signaling and gene expression changes
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Identify Unfavorable COVID Medicine Reactions from the Three-Dimensional Structure by Employing Convolutional Neural Network
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Improving Medication Prescription Strategies for Discordant Chronic Comorbidities Through Medical Data Bench-Marking and Recommender Systems
inferred← citeshttps://doi.org/10.1007/s10462-023-10413-7openalex-snapshot
An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects
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A Science-Based Methodology Framework for the Assessment of Combination Safety Risks in Clinical Trials
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Improving drug effectiveness using a second-generation artificial intelligence system based on the constrained-disorder principle to incorporate variability into drug design and administration: a perspective
inferred← citeshttps://doi.org/10.1016/b978-0-323-89925-3.00005-8openalex-snapshot
Artificial neural network (ANN) in drug delivery
inferred← citeshttps://doi.org/10.1016/j.bspc.2026.110934openalex-snapshot
Prediction of adverse drug reactions using gene expression data and neural networks with synthetic data augmentation
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On the road to explainable AI in drug-drug interactions prediction: A systematic review
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Metapath-based feature aggregated heterogeneous graph neural network for adverse drug reactions prediction
inferred← citeshttps://doi.org/10.1093/bib/bbab580openalex-snapshot
ADENet: a novel network-based inference method for prediction of drug adverse events
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Machine Learning Algorithms for Side Effects Prediction: A Comparative Analysis
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Evaluating the Effectiveness of Artificial Intelligence in Predicting Adverse Drug Reactions among Cancer Patients: A Systematic Review and Meta-Analysis
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Interactome: A Platform for Comprehensive Drug-Drug Interaction Analysis
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Predictive Analytics for Anticipating Adverse Drug Reactions to ameliorate Drug Development and Prescription Practices
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BERT-D2: Drug-Drug Interaction Extraction using BERT
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MLCNN-LSTM-COV: A Multilabel Convolutional Neural Network and Long Short-Term Memory-Based Framework for Predicting Adverse COVID Drug Reactions
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Prediction of Adverse Effects of Drug–Drug Interactions on Cardiovascular System Based on the Analysis of Structure–Activity Relationships
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DeSIDE-DDI: interpretable prediction of drug-drug interactions using drug-induced gene expressions
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Metapath-based Feature Aggregated Heterogeneous Graph Neural Network for Adverse Drug Reactions Predicting
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Prediction of adverse effects of drug-drug interactions on the cardiovascular system based on the analysis of structure-activity relationships
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Prediction of adverse drug reactions based on pharmacogenomics combination features: a preliminary study
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Artificial Intelligence and Machine Learning in Drug Discovery
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MLCNN‐COV: A multilabel convolutional neural network‐based framework to identify negative COVID medicine responses from the chemical three‐dimensional conformer
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Inceptionv3‐LSTM‐COV: A multi‐label framework for identifying adverse reactions to COVID medicine from chemical conformers based on Inceptionv3 and long short‐term memory
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Additional file 1 of DeSIDE-DDI: interpretable prediction of drug-drug interactions using drug-induced gene expressions
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