Results 31 to 40 of about 5,508,972 (143)

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

A target repurposing approach identifies N-myristoyltransferase as a new candidate drug target in filarial nematodes [PDF]

, 2014
Myristoylation is a lipid modification involving the addition of a 14-carbon unsaturated fatty acid, myristic acid, to the N-terminal glycine of a subset of proteins, a modification that promotes their binding to cell membranes for varied biological ...
Villemaine Estelle, Brendan D Galvin, Wyatt, Paul G., Zhiru Li, Estelle Villemaine, Brendan D. Galvin (592211), Carlow, Clotilde K. S., Wyatt Paul G., Zhiru Li (295010), Poole Catherine B., Li, Zhiru, Carlow Clotilde K. S., Michael P. Pollastri (345704), Clotilde K S Carlow, Michael P Pollastri, Li Zhiru, Chapman Melissa S., Melissa S. Chapman (625936), Melissa S Chapman, Chapman, Melissa S., Pollastri, Michael P., Clotilde K. S. Carlow (111245), Catherine B. Poole (111223), Paul G. Wyatt (473658), Pollastri Michael P., Poole, Catherine B., Catherine B Poole, Galvin, Brendan D., Villemaine, Estelle, Paul G Wyatt, Galvin Brendan D., Estelle Villemaine (625935)   +31 more
core   +2 more sources

Improving drug-target affinity prediction via feature fusion and knowledge distillation

Briefings Bioinform., 2023
Rapid and accurate prediction of drug-target affinity can accelerate and improve the drug discovery process. Recent studies show that deep learning models may have the potential to provide fast and accurate drug-target affinity prediction.
Rui-Qiang Lu, Jun Wang, Peng-Yong Li, Yu-Quan Li, Shuoyan Tan, Yiting Pan, Huan-Xiang Liu, Peng Gao, G. Xie, X. Yao   +9 more
semanticscholar   +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

Local and global modes of drug action in biochemical networks [PDF]

, 2008
It becomes increasingly accepted that a shift is needed from the traditional target-based approach of drug development to an integrated perspective of drug action in biochemical systems. We here present an integrative analysis of the interactions between
Schwartz, Jean Marc; id_orcid, Jean-Marc Schwartz, Nacher, Jose C., Jose C. Nacher, Jose C Nacher   +4 more
core   +1 more source

Enhanced information cross-attention fusion for drug–target binding affinity prediction [PDF]

PeerJ Computer Science
Background The rapid development of artificial intelligence has permeated many fields, with its application in drug discovery becoming increasingly mature.
Ailu Fei, Yihan Wang, Tiantian Ruan, Yekang Zhang, Min Yao, Li Wang   +5 more
doaj   +2 more sources

DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation

Nature Communications
Identifying novel drugs that can interact with target proteins is a highly challenging, time-consuming, and costly task in drug discovery and development. Numerous machine learning-based models have recently been utilized to accelerate the drug discovery
Pir Masoom Shah, Hui Zhu, Zhangli Lu, Kai-Li Wang, Jing Tang, Min Li   +5 more
semanticscholar   +1 more source

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

BMC Bioinformatics, 2023
Background Obtaining accurate drug–target binding affinity (DTA) information is significant for drug discovery and drug repositioning. Although some methods have been proposed for predicting DTA, the features of proteins and drugs still need to be ...
Haixia Zhai, Hongli Hou, Junwei Luo, Xiaoyan Liu, Zhengjiang Wu, Junfeng Wang   +5 more
doaj   +1 more source