MEDICASCY: A Machine Learning Approach for Predicting Small Molecule Drug Side Effects, Indications, Efficacy and Mode of Action.
Sign Up to like & getrecommendations! Published in 2020 at "Molecular pharmaceutics"
DOI: 10.1021/acs.molpharmaceut.9b01248
Abstract: To improve drug discovery yield, a method which is implemented at the beginning of drug discovery that accurately predicts drug side effects, indications, efficacy, and mode of action based solely on the input of the… read more here.
Keywords: drug side; drug; mode action; side ... See more keywords
Predicting the frequencies of drug side effects
Sign Up to like & getrecommendations! Published in 2020 at "Nature Communications"
DOI: 10.1038/s41467-020-18305-y
Abstract: A central issue in drug risk-benefit assessment is identifying frequencies of side effects in humans. Currently, frequencies are experimentally determined in randomised controlled clinical trials. We present a machine learning framework for computationally predicting frequencies… read more here.
Keywords: predicting frequencies; frequencies drug; drug side; drug ... See more keywords
A similarity-based deep learning approach for determining the frequencies of drug side effects.
Sign Up to like & getrecommendations! Published in 2021 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbab449
Abstract: The side effects of drugs present growing concern attention in the healthcare system. Accurately identifying the side effects of drugs is very important for drug development and risk assessment. Some computational models have been developed… read more here.
Keywords: side effect; drug side; side; side effects ... See more keywords
DSGAT: predicting frequencies of drug side effects by graph attention networks
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbab586
Abstract: A critical issue of drug risk-benefit evaluation is to determine the frequencies of drug side effects. Randomized controlled trail is the conventional method for obtaining the frequencies of side effects, while it is laborious and… read more here.
Keywords: frequencies drug; side effects; drug side; predicting frequencies ... See more keywords
Predicting Drug Interaction With Adenosine Receptors Using Machine Learning and SMOTE Techniques
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2946314
Abstract: Cancer is one of the most influential factors causing death in the world. Adenosine which is a molecule, found in all human cells by coupling with G protein it turns into an adenosine receptor. Adenosine… read more here.
Keywords: adenosine receptors; drug; machine; adenosine ... See more keywords
Identification of Drug-Side Effect Association via Semisupervised Model and Multiple Kernel Learning
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2018.2883834
Abstract: Drug-side effect association contains the information on marketed medicines and their recorded adverse drug reactions. Traditional experimental method is time consuming and expensive. All associations of drugs and side-effects are seen as a bipartite network.… read more here.
Keywords: drug; effect association; side effect; drug side ... See more keywords
Modular Multi–Source Prediction of Drug Side–Effects With DruGNN
Sign Up to like & getrecommendations! Published in 2022 at "IEEE/ACM Transactions on Computational Biology and Bioinformatics"
DOI: 10.1109/tcbb.2022.3175362
Abstract: Drug Side–Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side–effects, before their occurrence, is fundamental to reduce this impact, in particular on drug… read more here.
Keywords: multi source; modular multi; side effects; drug side ... See more keywords