Results 31 to 40 of about 400,933 (233)

Prediction of Drug-Target Affinity Using Attention Neural Network. [PDF]

Int J Mol Sci
Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it ...
Tang X, Lei X, Zhang Y.
europepmc   +2 more sources

GANsDTA: Predicting Drug-Target Binding Affinity Using GANs

Frontiers in Genetics, 2020
The computational prediction of interactions between drugs and targets is a standing challenge in drug discovery. State-of-the-art methods for drug-target interaction prediction are primarily based on supervised machine learning with known label ...
Lingling Zhao, Junjie Wang, Long Pang, Yang Liu, Jun Zhang   +4 more
doaj   +1 more source

Drug-target affinity prediction using applicability domain based on data density

, 2021
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.
Shunya, Sugita, Masahito, Ohue
core   +1 more source

Mitigating cold-start problems in drug-target affinity prediction with interaction knowledge transferring. [PDF]

Brief Bioinform, 2022
Predicting the drug-target interaction is crucial for drug discovery as well as drug repurposing. Machine learning is commonly used in drug-target affinity (DTA) problem.
Nguyen TM, Nguyen T, Tran T.
europepmc   +2 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

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   +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, Tayara, Hilal, Hilal Tayara, Zhenyu Xuan, Kil To Chong, Ashutosh Ghimire, Xuan, Zhenyu, Ghimire, Ashutosh   +7 more
core   +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

A Multibranch Neural Network for Drug-Target Affinity Prediction Using Similarity Information [PDF]

ACS Omega
Jing Chen, Xiaolin Yang, Haoyu Wu
doaj   +2 more sources

AffinityVAE: A multi-objective model for protein-ligand affinity prediction and drug design

, 2023
In the prediction of protein-ligand affinity, the traditional methods require a large amount of computing resources, and have certain limitations in predicting and simulating the structural changes.
Yu, X, Han, K, Wang, M, Li, W, Luo, Y, Wang, C, Jin, Q   +6 more
core   +1 more source