Artificial intelligence-driven prediction of multiple drug interactions
S Chen, T Li, L Yang, F Zhai, X Jiang… - Briefings in …, 2022 - academic.oup.com
When a drug is administered to exert its efficacy, it will encounter multiple barriers and go
through multiple interactions. Predicting the drug-related multiple interactions is critical for …
through multiple interactions. Predicting the drug-related multiple interactions is critical for …
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 …
A machine learning framework for predicting drug–drug interactions
S Mei, K Zhang - Scientific Reports, 2021 - nature.com
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 …
events before clinical drug co-prescription. Existing methods, commonly integrating …
Drug–drug interaction prediction: databases, web servers and computational models
In clinical treatment, two or more drugs (ie drug combination) are simultaneously or
successively used for therapy with the purpose of primarily enhancing the therapeutic …
successively used for therapy with the purpose of primarily enhancing the therapeutic …
A probabilistic approach for collective similarity-based drug–drug interaction prediction
Motivation: As concurrent use of multiple medications becomes ubiquitous among patients, it
is crucial to characterize both adverse and synergistic interactions between drugs. Statistical …
is crucial to characterize both adverse and synergistic interactions between drugs. Statistical …
Application of machine learning for drug–target interaction prediction
L Xu, X Ru, R Song - Frontiers in genetics, 2021 - frontiersin.org
Exploring drug–target interactions by biomedical experiments requires a lot of human,
financial, and material resources. To save time and cost to meet the needs of the present …
financial, and material resources. To save time and cost to meet the needs of the present …
Recent development of machine learning models for the prediction of drug-drug interactions
E Hong, J Jeon, HU Kim - Korean Journal of Chemical Engineering, 2023 - Springer
Polypharmacy, the co-administration of multiple drugs, has become an area of concern as
the elderly population grows and an unexpected infection, such as COVID-19 pandemic …
the elderly population grows and an unexpected infection, such as COVID-19 pandemic …
Recent advances in the machine learning-based drug-target interaction prediction
W Zhang, W Lin, D Zhang, S Wang… - Current drug …, 2019 - ingentaconnect.com
Background: The identification of drug-target interactions is a crucial issue in drug discovery.
In recent years, researchers have made great efforts on the drug-target interaction …
In recent years, researchers have made great efforts on the drug-target interaction …
A review of approaches for predicting drug–drug interactions based on machine learning
K Han, P Cao, Y Wang, F Xie, J Ma, M Yu… - Frontiers in …, 2022 - frontiersin.org
Drug–drug interactions play a vital role in drug research. However, they may also cause
adverse reactions in patients, with serious consequences. Manual detection of drug–drug …
adverse reactions in patients, with serious consequences. Manual detection of drug–drug …
Comprehensive Review of Drug–Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities
NN Wang, B Zhu, XL Li, S Liu, JY Shi… - Journal of Chemical …, 2023 - ACS Publications
Detecting drug–drug interactions (DDIs) is an essential step in drug development and drug
administration. Given the shortcomings of current experimental methods, the machine …
administration. Given the shortcomings of current experimental methods, the machine …