Results 21 to 30 of about 159,394 (268)

MEGDTA: multi-modal drug-target affinity prediction based on protein three-dimensional structure and ensemble graph neural network [PDF]

BMC Genomics
Background Drug development is a time-consuming and costly endeavor, and utilizing computer-aided methods to predict drug-target affinity (DTA) can significantly accelerate this process.
Zhanwei Hou, Yijun Li, Haixia Zhai, Junwei Luo, Yulian Ding, Yi Pan   +5 more
doaj   +2 more sources

Drug-target binding affinity prediction based on power graph and word2vec

BMC Medical Genomics
Background Drug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development.
Jing Hu, Shuo Hu, Minghao Xia, Kangxing Zheng, Xiaolong Zhang   +4 more
doaj   +3 more sources

DTBind: A Mechanism-Driven Deep Learning Framework for Accurate Prediction of Drug–Target Molecular Recognition [PDF]

Research
Accurate prediction of drug–target molecular recognition is essential for early-stage drug discovery, spanning binding occurrence, binding site localization, and binding affinity estimation.
Qiuyu Li, Zeyu Xu, Yanhao Zhu, Wanyun Zhou, Mingming Guan, Shiqing Zhao, Mengyuan Liu, Bin Liu, Juntao Liu   +8 more
doaj   +2 more sources

Learnable protein representations in computational biology for predicting drug-target affinity [PDF]

Journal of Cheminformatics
In this review, we discuss the various different types of learnable protein representations that have been used in computational biology, with a particular focus on representations that have been used in the paradigm of predicting drug-target affinity ...
Rachit Kumar, Joseph Romano, Marylyn Ritchie   +2 more
doaj   +2 more sources

Dual modality feature fused neural network integrating binding site information for drug target affinity prediction [PDF]

npj Digital Medicine
Accurately predicting binding affinities between drugs and targets is crucial for drug discovery but remains challenging due to the complexity of modeling interactions between small drug and large targets.
Haohuai He, Guanxing Chen, Zhenchao Tang, Calvin Yu-Chian Chen   +3 more
doaj   +2 more sources

Recent trends in machine learning and deep learning-based prediction of G-protein coupled receptor-ligand binding affinities [PDF]

Frontiers in Bioinformatics
Accurately predicting protein-ligand binding affinity is key in drug discovery. Machine Learning and Deep Learning methods used in the drug discovery process have advanced the prediction of drug–target binding affinities, particularly for G protein ...
Joshua Stephenson, Konda Reddy Karnati
doaj   +2 more sources

Deep drug-target binding affinity prediction with multiple attention blocks [PDF]

Briefings in Bioinformatics, 2021
Abstract Drug-target interaction (DTI) prediction has drawn increasing interest due to its substantial position in the drug discovery process. Many studies have introduced computational models to treat DTI prediction as a regression task, which directly predict the binding affinity of drug-target pairs.
Yuni, Zeng, Xiangru, Chen, Yujie, Luo, Xuedong, Li, Dezhong, Peng   +4 more
openaire   +2 more sources

Drug-Target Binding Affinity Prediction Using Transformers [PDF]

, 2021
AbstractDrug 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

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

DeepDTA: deep drug–target binding affinity prediction [PDF]

Bioinformatics, 2018
Abstract Motivation The identification of novel drug–target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT ...
Öztürk, Hakime, Ozkirimli, Elif, Özgür, Arzucan   +2 more
openaire   +3 more sources