INTRODUCTION: Drug-metabolizing enzymes (DMEs) and transporters (DTs) play integral roles in drug metabolism and drug-drug interactions (DDIs) which directly impact drug efficacy and safety. It is well-established that inhibition of DMEs and DTs ofte...
Problemy sotsial'noi gigieny, zdravookhraneniia i istorii meditsiny
40349243
The chronic cardiac deficiency continues to be one of the leading health care problems requiring innovative solutions. The article presents mathematical algorithm to evaluate drug interactions and targeted to minimize side effects and to optimize chr...
Drug-drug interactions (DDIs) are an important concern in the clinical practice and drug development process as these may lead to serious adverse effects on patient safety. Thorough DDI prediction is important for effective medication management and ...
Drug-drug interaction (DDI) is important in drug research and are one of the major causes of morbidity and mortality. The deep learning methods can automatically extract drug features from molecular graphs or drug-related networks, which improves the...
Drug drug Interactions (DDI) present considerable challenges in healthcare, often resulting in adverse effects or decreased therapeutic efficacy. This article proposes a novel deep sequential learning architecture called DDINet to predict and classif...
Journal of chemical information and modeling
40067127
Drug-drug interactions influence drug efficacy and patient prognosis, providing substantial research value. Some existing methods struggle with the challenges posed by sparse networks or lack the capability to integrate data from multiple sources. In...
BACKGROUND: While the COVID-19 pandemic has induced massive discussion of available medications on social media, traditional studies focused only on limited aspects, such as public opinions, and endured reporting biases, inefficiency, and long collec...
Drug-drug interactions (DDI) refer to the compound effects that occur when patients take multiple drugs simultaneously, which may reduce the drug efficacy and even harm the patient's health. Therefore, DDI prediction is significant for drug developme...
Molecular representation learning is pivotal in predicting molecular properties and advancing drug design. Traditional methodologies, which predominantly rely on homogeneous graph encoding, are limited by their inability to integrate external knowled...
Journal of chemical information and modeling
40310752
Drug-drug interaction (DDI) can compromise therapeutic efficacy and cause detrimental effects in polypharmacy. Computational prediction of DDI has emerged as an alternative approach to time-consuming clinical experiments for investigating potential d...