Results 221 to 230 of about 401,503 (258)

Breaking the barriers of data scarcity in drug–target affinity prediction

Briefings in Bioinformatics, 2023
Abstract Accurate prediction of drug–target affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities. While wet experiments remain the most reliable method, they are time-consuming and resource-intensive,
Qizhi Pei, Lijun Wu, Jinhua Zhu
exaly   +3 more sources

CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention

International Journal of Molecular Sciences, 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.
Ashutosh Ghimire, Hilal Tayara, Zhenyu Xuan   +2 more
exaly   +2 more sources

A Framework for Improving the Generalizability of Drug–Target Affinity Prediction Models

Journal of Computational Biology, 2023
Statistical models that accurately predict the binding affinity of an input ligand-protein pair can greatly accelerate drug discovery. Such models are trained on available ligand-protein interaction data sets, which may contain biases that lead the predictor models to learn data set-specific, spurious patterns instead of generalizable relationships ...
Riza Özçelik, Alperen Bag, Berk Atil, Melih Barsbey, Arzucan Özgür, Elif Ozkirimli 0001   +5 more
openaire   +2 more sources

Affinity-Based Methods in Drug-Target Discovery

Current Drug Targets, 2015
Target discovery using the molecular approach, as opposed to the more traditional systems approach requires the study of the cellular or biological process underlying a condition or disease. The approaches that are employed by the "bench" scientist may be genetic, genomic or proteomic and each has its rightful place in the drug-target discovery process.
Gabriela, Rylova, Tomas, Ozdian, Lakshman, Varanasi, Miroslav, Soural, Jan, Hlavac, Dusan, Holub, Petr, Dzubak, Marian, Hajduch   +7 more
openaire   +2 more sources

Modality-DTA: Multimodality Fusion Strategy for Drug–Target Affinity Prediction

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023
Prediction of the drug-target affinity (DTA) plays an important role in drug discovery. Existing deep learning methods for DTA prediction typically leverage a single modality, namely simplified molecular input line entry specification (SMILES) or amino acid sequence to learn representations.
Xixi Yang, Zhangming Niu, Yuansheng Liu, Bosheng Song, Weiqiang Lu, Li Zeng, Xiangxiang Zeng   +6 more
openaire   +2 more sources

A Mutual Attention Model for Drug Target Binding Affinity Prediction

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022
Vrious machine learning approaches have been developed for drug-target interaction (DTI) prediction. One class of these approaches, DTBA, is interested in Drug-Target Binding Affinity strength, rather than focusing merely on the presence or absence of interaction. Several machine learning methods have been developed for this purpose.
openaire   +2 more sources

Multimodal contrastive representation learning for drug-target binding affinity prediction

Methods, 2023
In the biomedical field, the efficacy of most drugs is demonstrated by their interactions with targets, meanwhile, accurate prediction of the strength of drug-target binding is extremely important for drug development efforts. Traditional bioassay-based drug-target binding affinity (DTA) prediction methods cannot meet the needs of drug R&D in the era ...
Linlin, Zhang, Chunping, Ouyang, Yongbin, Liu, Yiming, Liao, Zheng, Gao   +4 more
openaire   +2 more sources

DHAG-DTA: Dynamic Hierarchical Affinity Graph Model for Drug-Target Binding Affinity Prediction

IEEE Transactions on Computational Biology and Bioinformatics
Computational methods for predicting drug-target binding affinity (DTA) are critical for large-scale screening of prospective therapeutic compounds during drug discovery. Deep neural networks (DNNs) have recently shown significant promise for DTA prediction.
Cheng Wang, Yang Liu, Shitao Song, Kun Cao, Xiaoyan Liu, Gaurav Sharma, Maozu Guo   +6 more
openaire   +2 more sources

Contrastive Meta-Learning for Drug-Target Binding Affinity Prediction

2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022
Mei Li, Sihan Xu, Xiangrui Cai, Zhong Zhang 0001, Hua Ji   +4 more
openaire   +1 more source