Results 21 to 30 of about 6,076,909 (260)

Prediction of Drug-Target Affinity Using Attention Neural Network

International Journal of Molecular Sciences
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 challenging to meet the current demands for swift and efficient drug development. Consequently, computational
Tang, Xin, Lei, Xiujuan, Zhang, Yuchen
openaire   +3 more sources

FingerDTA: A Fingerprint-Embedding Framework for Drug-Target Binding Affinity Prediction

Big Data Mining and Analytics, 2023
Many efforts have been exerted toward screening potential drugs for targets, and conducting wet experiments remains a laborious and time-consuming approach.
Xuekai Zhu, Juan Liu, Jian Zhang, Zhihui Yang, Feng Yang, Xiaolei Zhang   +5 more
doaj   +3 more sources

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   +3 more sources

GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion [PDF]

Scientific Reports
Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug
Youzhi Liu, Linlin Xing, Longbo Zhang, Hongzhen Cai, Maozu Guo   +4 more
doaj   +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   +3 more sources

Drug–Target Affinity Prediction Based on Cross-Modal Fusion of Text and Graph

Applied Sciences
Drug–target affinity (DTA) prediction is a critical step in virtual screening and significantly accelerates drug development. However, existing deep learning-based methods relying on single-modal representations (e.g., text or graphs) struggle to fully ...
Jucheng Yang, Fushun Ren
doaj   +2 more sources

MDNN-DTA: a multimodal deep neural network for drug-target affinity prediction [PDF]

Frontiers in Genetics
Determining drug-target affinity (DTA) is a pivotal step in drug discovery, where in silico methods can significantly improve efficiency and reduce costs.
Xu Gao, Xu Gao, Mengfan Yan, Mengfan Yan, Chengwei Zhang, Chengwei Zhang, Gang Wu, Gang Wu, Jiandong Shang, Jiandong Shang, Congxiang Zhang, Congxiang Zhang, Kecheng Yang, Kecheng Yang   +13 more
doaj   +2 more sources

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

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, Huimin Zhu, Zhangli Lu, Kaili Wang, Jing Tang, Min Li   +5 more
doaj   +2 more sources

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

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   +3 more sources

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