Results 41 to 50 of about 4,932,082 (261)

Correction to: Breaking the barriers of data scarcity in drug-target affinity prediction. [PDF]

Brief Bioinform, 2023
europepmc   +3 more sources

Drug-Target Binding Affinity Prediction Using Transformers [PDF]

, 2021
Abstract Drug discovery is generally difficult, expensive, and low success rate. One of the essential steps in the early stages of drug discovery and drug repurposing is identifying drug-target interactions. Binding affinity indicates the strength of drug-target pair interactions.
Mahsa Saadat, Armin Behjati, Fatemeh Zare-Mirakabad, Sajjad Gharaghani   +3 more
openaire   +1 more source

Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinity

CoRR, 2022
The identification of drug-target binding affinity (DTA) has attracted increasing attention in the drug discovery process due to the more specific interpretation than binary interaction prediction. Recently, numerous deep learning-based computational methods have been proposed to predict the binding affinities between drugs and targets benefiting from ...
Zhaoyang Chu, Shichao Liu 0002, Wen Zhang 0008   +2 more
openaire   +3 more sources

DTA+VAE: Drug Target Affinity prediction with SELFIES String via variational autoencoder and Transformer6 protein model [PDF]

, 2023
A crucial step in drug discovery is identifying drug-target interactions. Over the years, there have been many computational methods to determine whether a drug and a target will interact or not.
Patel, Yakin
core   +1 more source

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, Elif Özkirimli Ölmez, Arzucan Özgür   +2 more
openaire   +2 more sources

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, Hang Le 0003, Thomas P. Quinn, Tri Nguyen, Thuc Duy Le, Svetha Venkatesh   +5 more
openaire   +2 more sources

Naphthoquinone derivatives exert their antitrypanosomal activity via a multi-target mechanism [PDF]

, 2013
Recently, we reported on a new class of naphthoquinone derivatives showing a promising anti-trypanosomatid profile in cell-based experiments. The lead of this series (B6, 2-phenoxy-1,4-naphthoquinone) showed an ED(50) of 80 nM against Trypanosoma brucei ...
Mazet Muriel, Muriel Mazet, Scapozza, L., Brun Reto, Bakker, Barbara M.; id_orcid, Leonardo Scapozza (102177), Leonardo Scapozza, Bergamini Christian, Remo Perozzo, Federica Prati, Bakker Barbara M., Bergamini, C., Barbara M. Bakker, Haanstra, J.R.; id_orcid, Cavalli Andrea, Capranico, G., Bakker, B.M., Jurgen R Haanstra, Mazet, M., Bakker, Barbara M., Haanstra, J.R., Capranico, Giovanni, Pieretti, S., Giovanni Capranico (102150), Haanstra, Jurgen R., Lenaz Giorgio, Bakker, Barbara M, Romana Fato (102127), Jurgen R. Haanstra, Michels, Paulus, Jurgen R. Haanstra (102071), Andrea Cavalli, Perozzo, Remo, Bergamini, Christian, Bolognesi, M.L., Pieretti, Simone, Reto Brun, Reto Brun (41511), Brun, R., Bolognesi Maria Laura, Fato, Romana, Perozzo, R., Simone Pieretti, Andrea Cavalli (102196), Giovanni Capranico, Michels, Paul A. M., Lenaz, G., Christian Bergamini (102106), Paul A. M. Michels, Scapozza Leonardo, Remo Perozzo (102094), Haanstra, Jurgen R, Muriel Mazet (102083), Giorgio Lenaz, Giorgio Lenaz (102138), Fato, R., Prati, Federica, Maria Laura Bolognesi, Barbara M. Bakker (102159), Cavalli, Andrea, Paul A M Michels, Pieretti Simone, Barbara M Bakker, Lenaz, Giorgio, Perozzo Remo, Prati, F., Michels Paul A. M., Haanstra Jurgen R., Christian Bergamini, Fato Romana, Michels, P.A.M., Paul A. M. Michels (102168), Romana Fato, Brun, Reto, Bolognesi, Maria Laura, Cavalli, A., Mazet, Muriel, Scapozza, Leonardo, Simone Pieretti (102060), Prati Federica, Maria Laura Bolognesi (102185), Capranico Giovanni, Federica Prati (102117)   +82 more
core   +3 more sources

Quantitative prediction model for affinity of drug–target interactions based on molecular vibrations and overall system of ligand-receptor

BMC Bioinformatics, 2021
Background The study of drug–target interactions (DTIs) affinity plays an important role in safety assessment and pharmacology. Currently, quantitative structure–activity relationship (QSAR) and molecular docking (MD) are most common methods in research ...
Xian-rui Wang, Ting-ting Cao, Cong Min Jia, Xue-mei Tian, Yun Wang   +4 more
doaj   +1 more source

Target identification of small molecules: an overview of the current applications in drug discovery

BMC Biotechnology, 2023
Target identification is an essential part of the drug discovery and development process, and its efficacy plays a crucial role in the success of any given therapy.
Yasser Tabana, Dinesh Babu, Richard Fahlman, Arno G. Siraki, Khaled Barakat   +4 more
doaj   +1 more source

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, Zhen-Yu Hong, Insuk Sohn, Changha Hwang   +3 more
doaj   +1 more source