BMC Bioinformatics, 2022 Predicting drug–target interactions (DTIs) has become an important bioinformatics issue because it is one of the critical and preliminary stages of drug repositioning.
Reza Hassanzadeh +1 more
doaj +1 more source
Drug design for CNS diseases: polypharmacological profiling of compounds using cheminformatic, 3D-QSAR and virtual screening methodologies [PDF]
, 2016 Support was kindly provided by the EU COST Action CM1103. DA, KN, and JV kindly acknowledge national project number 172033 and OI1612039 supported by the Ministry of the Republic of Serbia.
Agbaba, Danica +7 more
core +3 more sources
Autoencoders for Drug-Target Interaction Prediction [PDF]
, 2020 Abstract Background: Because it is so laborious and expensive to experimentally identify Drug-Target Interactions (DTIs), only a few DTIs have been verified. Computational methods are useful for identifying DTIs in biological studies of drug discovery and development.
Ming Chen, Xiuze Zhou
openaire +1 more source
Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction. [PDF]
PLoS Computational Biology, 2016 De novo experimental drug discovery is an expensive and time-consuming task. It requires the identification of drug-target interactions (DTIs) towards targets of biological interest, either to inhibit or enhance a specific molecular function.
Edgar D Coelho +2 more
doaj +1 more source
Electrostatic Steering Accelerates C3d:CR2 Association. [PDF]
, 2016 Electrostatic effects are ubiquitous in protein interactions and are found to be pervasive in the complement system as well. The interaction between complement fragment C3d and complement receptor 2 (CR2) has evolved to become a link between innate and ...
Dimitrios Morikis +4 more
core +2 more sources
Drug–target interaction prediction based on protein features, using wrapper feature selection
Scientific Reports, 2023 Drug–target interaction prediction is a vital stage in drug development, involving lots of methods. Experimental methods that identify these relationships on the basis of clinical remedies are time-taking, costly, laborious, and complex introducing a lot
Hengame Abbasi Mesrabadi +2 more
semanticscholar +1 more source
Discovering drug–target interaction knowledge from biomedical literature
Bioinformatics, 2022 Abstract Motivation The interaction between drugs and targets (DTI) in human body plays a crucial role in biomedical science and applications. As millions of papers come out every year in the biomedical domain, automatically discovering DTI knowledge from biomedical literature, which are usually ...
Yutai Hou +7 more
openaire +2 more sources
Differential bioreactivity of neutral, cationic and anionic polystyrene nanoparticles with cells from the human alveolar compartment: robust response of alveolar type 1 epithelial cells [PDF]
, 2015 BACKGROUND: Engineered nanoparticles (NP) are being developed for inhaled drug delivery. This route is non-invasive and the major target; alveolar epithelium provides a large surface area for drug administration and absorption, without first pass ...
Ruenraroengsak, P, Tetley, TD
core +2 more sources
Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network. [PDF]
, 2019 Traditional Chinese Medicine (TCM) has received increasing attention as a complementary approach or alternative to modern medicine. However, experimental methods for identifying novel targets of TCM herbs heavily relied on the current available herb ...
Chen, Jianxin +11 more
core +1 more source
PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions [PDF]
Chemical Science, 2020 Recently, deep neural network (DNN)-based drug–target interaction (DTI) models were highlighted for their high accuracy with affordable computational costs.
Seokhyun Moon +4 more
semanticscholar +1 more source