Results 91 to 100 of about 333,304 (136)

A Generalized Minimal PBPK-PD Model of Bispecific Antibodies: Case Studies and Applications in Drug Development. [PDF]

CPT Pharmacometrics Syst Pharmacol
Spinosa P, Joslyn L, Ramanujan S, Gadkar K, Hosseini I.   +4 more
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Optimization of galectin-3 binding agents by in situ multiple compound synthesis and native mass spectrometry. [PDF]

Sci Rep
Hoshi K, Konuma T, Taguchi R, Akashi S, Katahira Y, Katsuyama A, Ichikawa S.   +6 more
europepmc   +1 more source

Unlocking the potential of EphA2 with precision-guided cancer therapy: bicycle drug conjugates. [PDF]

J Transl Med
Bennett G, Riedl J, Mudd G.
europepmc   +1 more source

GraphDTA for predicting drug–target binding affinity

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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
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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
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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
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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.
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NG-DTA: Drug-target affinity prediction with n-gram molecular graphs

2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023
Drug-target affinity (DTA) prediction is crucial to speed up drug development. The advance in deep learning allows accurate DTA prediction. However, most deep learning methods treat protein as a 1D string which is not informative to models compared to a graph representation. In this paper, we present a deep-learning-based DTA prediction method called N-
Lok-In, Tsui, Te-Cheng, Hsu, Che, Lin
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