Results 41 to 50 of about 2,729,950 (336)
Nature Communications, 2021 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.
Qing Ye +7 more
semanticscholar +1 more source
BMC Bioinformatics, 2021 Background Wet-lab experiments for identification of interactions between drugs and target proteins are time-consuming, costly and labor-intensive. The use of computational prediction of drug–target interactions (DTIs), which is one of the significant ...
Ali Ghanbari Sorkhi +3 more
doaj +1 more source
Structure based de novo design of IspD inhibitors as anti-tubercular agents [PDF]
, 2012 Tuberculosis is one of the leading contagious diseases, caused by Mycobacterium tuberculosis. Despite improvements in anti-tubercular agents, it remains one of the most prevalent infectious diseases worldwide, responsible for a total of 1.6 million ...
Abhay T. Sangamwar +4 more
core +2 more sources
RyR1-targeted drug discovery pipeline integrating FRET-based high-throughput screening and human myofiber dynamic Ca2+ assays. [PDF]
, 2020 Elevated cytoplasmic [Ca2+] is characteristic in severe skeletal and cardiac myopathies, diabetes, and neurodegeneration, and partly results from increased Ca2+ leak from sarcoplasmic reticulum stores via dysregulated ryanodine receptor (RyR) channels ...
Bers, Donald M +6 more
core +2 more sources
A semi-supervised method for drug-target interaction prediction with consistency in networks. [PDF]
PLoS ONE, 2013 Computational prediction of interactions between drugs and their target proteins is of great importance for drug discovery and design. The difficulties of developing computational methods for the prediction of such potential interactions lie in the ...
Hailin Chen, Zuping Zhang
doaj +1 more source
A Novel Deep Neural Network Technique for Drug–Target Interaction
Pharmaceutics, 2022 Drug discovery (DD) is a time-consuming and expensive process. Thus, the industry employs strategies such as drug repositioning and drug repurposing, which allows the application of already approved drugs to treat a different disease, as occurred in the ...
Jackson G. de Souza +2 more
doaj +1 more source
Composite spheres made of bioengineered spider silk and iron oxide nanoparticles for theranostics applications [PDF]
, 2019 Bioengineered spider silk is a biomaterial that has exquisite mechanical properties, biocompatibility, and biodegradability. Iron oxide nanoparticles can be applied for the detection and analysis of biomolecules, target drug delivery, as MRI contrast ...
Dams-Kozlowska, Hanna +7 more
core +1 more source
DeepPurpose: a deep learning library for drug–target interaction prediction
Bioinform., 2020 Summary Accurate prediction of drug–target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists
Kexin Huang +5 more
semanticscholar +1 more source
Allo-network drugs: Extension of the allosteric drug concept to protein-protein interaction and signaling networks [PDF]
, 2013 Allosteric drugs are usually more specific and have fewer side effects than orthosteric drugs targeting the same protein. Here, we overview the current knowledge on allosteric signal transmission from the network point of view, and show that most ...
Csermely, Péter +2 more
core +2 more sources
Biases of Drug–Target Interaction Network Data [PDF]
, 2014 Network based prediction of interaction between drug compounds and target proteins is a core step in the drug discovery process. The availability of drugtarget interaction data has boosted the development of machine learning methods for the in silico prediction of drugtarget interactions.
Laarhoven, T. van, Marchiori, E.
openaire +2 more sources