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Pharmaceutical Preparations

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Graph Convolutional Networks for Drug Response Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
BACKGROUND: Drug response prediction is an important problem in computational personalized medicine. Many machine-learning-based methods, especially deep learning-based ones, have been proposed for this task. However, these methods often represent th...

Graph Convolutional Autoencoder and Generative Adversarial Network-Based Method for Predicting Drug-Target Interactions.

IEEE/ACM transactions on computational biology and bioinformatics
The computational prediction of novel drug-target interactions (DTIs) may effectively speed up the process of drug repositioning and reduce its costs. Most previous methods integrated multiple kinds of connections about drugs and targets by construct...

Predicting Drug-Drug Interactions Based on Integrated Similarity and Semi-Supervised Learning.

IEEE/ACM transactions on computational biology and bioinformatics
A drug-drug interaction (DDI) is defined as an association between two drugs where the pharmacological effects of a drug are influenced by another drug. Positive DDIs can usually improve the therapeutic effects of patients, but negative DDIs cause th...

Analysis of Clinical Parameters, Drug Consumption and Use of Health Resources in a Southern European Population with Alcohol Abuse Disorder during COVID-19 Pandemic.

International journal of environmental research and public health
The disruption in healthcare attention to people with alcohol dependence, along with psychological decompensation as a consequence of lockdown derived from the COVID-19 pandemic could have a negative impact on people who suffer from alcohol abuse dis...

Ontology-based identification and prioritization of candidate drugs for epilepsy from literature.

Journal of biomedical semantics
BACKGROUND: Drug repurposing can improve the return of investment as it finds new uses for existing drugs. Literature-based analyses exploit factual knowledge on drugs and diseases, e.g. from databases, and combine it with information from scholarly ...

Prediction of Drug-Target Interactions Based on Network Representation Learning and Ensemble Learning.

IEEE/ACM transactions on computational biology and bioinformatics
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...

Drug-Target Interaction Prediction: End-to-End Deep Learning Approach.

IEEE/ACM transactions on computational biology and bioinformatics
The discovery of potential Drug-Target Interactions (DTIs) is a determining step in the drug discovery and repositioning process, as the effectiveness of the currently available antibiotic treatment is declining. Although putting efforts on the tradi...

Picture-word order compound protein interaction: Predicting compound-protein interaction using structural images of compounds.

Journal of computational chemistry
Identifying potential associations between proteins and compounds is significant and challenging in the drug discovery process. Existing deep-learning-based methods tend to treat compounds and proteins as sequences or graphs. Inspired by the rapid de...

A unified drug-target interaction prediction framework based on knowledge graph and recommendation system.

Nature communications
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...

Combined deep learning and molecular docking simulations approach identifies potentially effective FDA approved drugs for repurposing against SARS-CoV-2.

Computers in biology and medicine
The ongoing pandemic of Coronavirus Disease 2019 (COVID-19) has posed a serious threat to global public health. Drug repurposing is a time-efficient approach to finding effective drugs against SARS-CoV-2 in this emergency. Here, we present a robust e...