AIMC Topic: Drug-Related Side Effects and Adverse Reactions
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Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction.
Nucleic acids research
Jul 5, 2024
Evaluating pharmacokinetic properties of small molecules is considered a key feature in most drug development and high-throughput screening processes. Generally, pharmacokinetics, which represent the fate of drugs in the human body, are described fro...
Identification and Characterization of Immune Checkpoint Inhibitor-Induced Toxicities From Electronic Health Records Using Natural Language Processing.
JCO clinical cancer informatics
Apr 1, 2024
PURPOSE: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet their use is associated with immune-related adverse events (irAEs). Estimating the prevalence and patient impact of these irAEs in the real-world data setting is c...
A Novel Deep Learning Model for Drug-drug Interactions.
Current computer-aided drug design
Jan 1, 2024
INTRODUCTION: Drug-drug interactions (DDIs) can lead to adverse events and compromised treatment efficacy that emphasize the need for accurate prediction and understanding of these interactions.
Current Scenario and Future Prospects of Adverse Drug Reactions (ADRs) Monitoring and Reporting Mechanisms in the Rural Areas of India.
Current drug safety
Jan 1, 2024
Pharmacovigilance (PV) deals with the detection, collection, assessment, understanding, and prevention of adverse effects associated with drugs. The objective of PV is to ensure the safety of the medicines and patients by monitoring and reporting all...
MSDRP: a deep learning model based on multisource data for predicting drug response.
Bioinformatics (Oxford, England)
Sep 2, 2023
MOTIVATION: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning a...
DKADE: a novel framework based on deep learning and knowledge graph for identifying adverse drug events and related medications.
Briefings in bioinformatics
Jul 20, 2023
Adverse drug events (ADEs) are common in clinical practice and can cause significant harm to patients and increase resource use. Natural language processing (NLP) has been applied to automate ADE detection, but NLP systems become less adaptable when ...
STNN-DDI: a Substructure-aware Tensor Neural Network to predict Drug-Drug Interactions.
Briefings in bioinformatics
Jul 18, 2022
Computational prediction of multiple-type drug-drug interaction (DDI) helps reduce unexpected side effects in poly-drug treatments. Although existing computational approaches achieve inspiring results, they ignore to study which local structures of d...
SPARSE: a sparse hypergraph neural network for learning multiple types of latent combinations to accurately predict drug-drug interactions.
Bioinformatics (Oxford, England)
Jun 24, 2022
MOTIVATION: Predicting side effects of drug-drug interactions (DDIs) is an important task in pharmacology. The state-of-the-art methods for DDI prediction use hypergraph neural networks to learn latent representations of drugs and side effects to exp...
Deep Learning Methods for Detecting Side Effects of Cancer Chemotherapies Reported in a Remote Monitoring Web Application.
Studies in health technology and informatics
May 25, 2022
The objective of our work was to develop deep learning methods for extracting and normalizing patient-reported free-text side effects in a cancer chemotherapy side effect remote monitoring web application. The F-measure was 0.79 for the medical conce...
3DGT-DDI: 3D graph and text based neural network for drug-drug interaction prediction.
Briefings in bioinformatics
May 13, 2022
MOTIVATION: Drug-drug interactions (DDIs) occur during the combination of drugs. Identifying potential DDI helps us to study the mechanism behind the combination medication or adverse reactions so as to avoid the side effects. Although many artificia...
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