Information retrieval and text mining technologies for chemistry
Efficient access to chemical information contained in scientific literature, patents, technical
reports, or the web is a pressing need shared by researchers and patent attorneys from …
reports, or the web is a pressing need shared by researchers and patent attorneys from …
Application of Artificial Intelligence in Drug–Drug Interactions Prediction: A Review
Y Zhang, Z Deng, X Xu, Y Feng… - Journal of chemical …, 2023 - ACS Publications
Drug–drug interactions (DDI) are a critical aspect of drug research that can have adverse
effects on patients and can lead to serious consequences. Predicting these events …
effects on patients and can lead to serious consequences. Predicting these events …
Deep learning improves prediction of drug–drug and drug–food interactions
Drug interactions, including drug–drug interactions (DDIs) and drug–food constituent
interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug …
interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug …
Data-driven prediction of drug effects and interactions
Adverse drug events remain a leading cause of morbidity and mortality around the world.
Many adverse events are not detected during clinical trials before a drug receives approval …
Many adverse events are not detected during clinical trials before a drug receives approval …
Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties
Abstract Objective Drug–drug interactions (DDIs) are an important consideration in both
drug development and clinical application, especially for co-administered medications …
drug development and clinical application, especially for co-administered medications …
Drug-drug interaction prediction based on knowledge graph embeddings and convolutional-LSTM network
Interference between pharmacological substances can cause serious medical injuries.
Correctly predicting so-called drug-drug interactions (DDI) does not only reduce these cases …
Correctly predicting so-called drug-drug interactions (DDI) does not only reduce these cases …
Machine learning algorithms to infer trait‐matching and predict species interactions in ecological networks
Ecologists have long suspected that species are more likely to interact if their traits match in
a particular way. For example, a pollination interaction may be more likely if the proportions …
a particular way. For example, a pollination interaction may be more likely if the proportions …
Similarity-based modeling in large-scale prediction of drug-drug interactions
Drug-drug interactions (DDIs) are a major cause of adverse drug effects and a public health
concern, as they increase hospital care expenses and reduce patients' quality of life. DDI …
concern, as they increase hospital care expenses and reduce patients' quality of life. DDI …
[HTML][HTML] Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives
Highlights•Review of the state-of-the-art in the field of compound combination modelling.•
Significance of quality control of large-scale combination screening data.•Strategies for …
Significance of quality control of large-scale combination screening data.•Strategies for …
[HTML][HTML] Computational prediction of drug-drug interactions based on drugs functional similarities
Therapeutic activities of drugs are often influenced by co-administration of drugs that may
cause inevitable drug-drug interactions (DDIs) and inadvertent side effects. Prediction and …
cause inevitable drug-drug interactions (DDIs) and inadvertent side effects. Prediction and …