AIMC Topic: Drug Interactions

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Graph neural network approaches for drug-target interactions.

Current opinion in structural biology Jan 21, 2022
Developing new drugs remains prohibitively expensive, time-consuming, and often involves safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug discovery process and thus facilitate drug development. Non-Euclidian da...
Drug Development Molecular Structure Drug Interactions Neural Networks, Computer Drug Discovery
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Prediction of Drug-Target Interactions Based on Network Representation Learning and Ensemble Learning.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2021
Identifying interactions between drugs and target proteins is a critical step in the drug development process, as it helps identify new targets for drugs and accelerate drug development. The number of known drug-protein interactions (positive samples...
Decision Trees Proteins Pharmaceutical Preparations Computational Biology Drug Development Drug Interactions Machine Learning Algorithms Humans
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A unified drug-target interaction prediction framework based on knowledge graph and recommendation system.

Nature communications Nov 22, 2021
Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfe...
Drug Repositioning Pharmaceutical Preparations Drug Interactions Models, Theoretical Drug Development Knowledge Drug-Related Side Effects and Adverse Reactions Pattern Recognition, Automated Machine Learning
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Predicting biochemical and physiological effects of natural products from molecular structures using machine learning.

Natural product reports Nov 17, 2021
Covering: 2016 to 2021Discovery of novel natural products has been greatly facilitated by advances in genome sequencing, genome mining and analytical techniques. As a result, the volume of data for natural products has increased over the years, which...
Molecular Structure Drug Interactions Machine Learning Biological Products
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Machine Learning-Based Prediction of Drug-Drug Interactions for Histamine Antagonist Using Hybrid Chemical Features.

Cells Nov 9, 2021
The requesting of detailed information on new drugs including drug-drug interactions or targets is often unavailable and resource-intensive in assessing adverse drug events. To shorten the common evaluation process of drug-drug interactions, we prese...
Drug Interactions Cytochrome P-450 Enzyme System Histamine Antagonists Databases as Topic Algorithms Reproducibility of Results ROC Curve Machine Learning
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Comparative analysis of network-based approaches and machine learning algorithms for predicting drug-target interactions.

Methods (San Diego, Calif.) Nov 1, 2021
Computational prediction of drug-target interactions (DTIs) is of particular importance in the process of drug repositioning because of its efficiency in selecting potential candidates for DTIs. A variety of computational methods for predicting DTIs ...
Machine Learning Algorithms Drug Interactions Proteins Drug Repositioning
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A machine learning framework for predicting drug-drug interactions.

Scientific reports Sep 2, 2021
Understanding drug-drug interactions is an essential step to reduce the risk of adverse drug events before clinical drug co-prescription. Existing methods, commonly integrating heterogeneous data to increase model performance, often suffer from a hig...
Drug Interactions Machine Learning Drug Discovery Humans Computational Biology Protein Interaction Maps Signal Transduction
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AttentionDDI: Siamese attention-based deep learning method for drug-drug interaction predictions.

BMC bioinformatics Aug 21, 2021
BACKGROUND: Drug-drug interactions (DDIs) refer to processes triggered by the administration of two or more drugs leading to side effects beyond those observed when drugs are administered by themselves. Due to the massive number of possible drug pair...
Pharmaceutical Preparations Neural Networks, Computer Humans Drug Interactions Deep Learning Drug-Related Side Effects and Adverse Reactions
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Excipient knowledgebase: Development of a comprehensive tool for understanding the disposition and interaction potential of common excipients.

CPT: pharmacometrics & systems pharmacology Aug 1, 2021
Although the use of excipients is widespread, a thorough understanding of the drug interaction potential of these compounds remains a frequent topic of current research. Not only can excipients alter the disposition of coformulated drugs, but it is l...
Humans Computer Simulation ATP Binding Cassette Transporter, Subfamily B, Member 1 Drug Interactions Models, Biological Excipients Glycerol Knowledge Bases Cytochrome P-450 CYP3A
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Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort.

PLoS computational biology Jul 6, 2021
Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of electronic health records (EHRs) offers a unique opportunity to build machine learning algorithms to identify drug-drug interactions that drive adverse e...
Adolescent Computational Biology Female Liver Algorithms Humans Drug Interactions Young Adult Aged Male Machine Learning Models, Statistical Retrospective Studies Chemical and Drug Induced Liver Injury Adult Aged, 80 and over Middle Aged Electronic Health Records Anti-Inflammatory Agents, Non-Steroidal
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