GraphDTA: Predicting drug–target binding affinity with graph neural networks [PDF]
Bioinformatics, 2019 Abstract The development of new drugs is costly, time consuming, and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs.
Thin Nguyen +5 more
openaire +2 more sources
WideDTA: prediction of drug-target binding affinity
CoRR, 2019 Motivation: Prediction of the interaction affinity between proteins and compounds is a major challenge in the drug discovery process. WideDTA is a deep-learning based prediction model that employs chemical and biological textual sequence information to predict binding affinity.
Hakime Öztürk +2 more
openaire +2 more sources
Prediction of drug–target binding affinity using similarity-based convolutional neural network
Scientific Reports, 2021 Identifying novel drug–target interactions (DTIs) plays an important role in drug discovery. Most of the computational methods developed for predicting DTIs use binary classification, whose goal is to determine whether or not a drug–target (DT) pair ...
Jooyong Shim +3 more
doaj +1 more source
MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs. [PDF]
BMC BiolAccurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also ...
Zeng X +6 more
europepmc +2 more sources
Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities [PDF]
Frontiers in Chemistry, 2019 The drug development is generally arduous, costly, and success rates are low. Thus, the identification of drug-target interactions (DTIs) has become a crucial step in early stages of drug discovery. Consequently, developing computational approaches capable of identifying potential DTIs with minimum error rate are increasingly being pursued.
Maha Thafar +6 more
openaire +4 more sources
Drug-target affinity prediction using applicability domain based on data density
, 2022 In the pursuit of research and development of drug discovery, the computational prediction of the target affinity of a drug candidate is useful for screening compounds at an early stage and for verifying the binding potential to an unknown target.
杉田, 駿也 +3 more
core +1 more source
CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention
, 2022 Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug–target affinity is crucial.
Chong, Kil To +7 more
core +1 more source
Biomolecules, 2021 The binding affinity of small molecules to receptor proteins is essential to drug discovery and drug repositioning. Chemical methods are often time-consuming and costly, and models for calculating the binding affinity are imperative.
Xun Wang +4 more
doaj +1 more source
"In silico" prediction of drug transport across physiological barriers [PDF]
, 2011 Physiological barriers maintain and safeguard homeostasis of certain body compartments by an increased resistance against free diffusion. Distribution and pharmacokinetics of drugs can be altered as well, if they have to cross these barriers in order to ...
Suenderhauf, Claudia
core +1 more source
Computational approaches for investigating protein-ligand interactions - towards an in-depth understanding of the dengue virus methyltransferase [PDF]
, 2013 Interactions between proteins and their ligands play crucial roles in many biological processes, such as metabolism, signaling, transport, regulation or molecular recognition. Understanding the molecular basis of protein-ligand interactions is thus of
Schmidt, Tobias Benjamin
core +1 more source