Results 31 to 40 of about 159,394 (268)
GraphDTA: Predicting drug–target binding affinity with graph neural networks [PDF]
Bioinformatics, 2019 AbstractThe development of new drugs is costly, time consuming, and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs. Computational
Thin Nguyen +5 more
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PLoS Computational Biology, 2023 Drug-target binding affinity prediction plays a key role in the early stage of drug discovery. Numerous experimental and data-driven approaches have been developed for predicting drug-target binding affinity.
Mahmood Kalemati +2 more
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Biomedicines, 2022 Drug-target binding affinity (DTA) prediction is an essential step in drug discovery. Drug-target protein binding occurs at specific regions between the protein and drug, rather than the entire protein and drug.
Haelee Bae, Hojung Nam
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Algebraic shortcuts for leave-one-out cross-validation in supervised network inference [PDF]
, 2020 Supervised machine learning techniques have traditionally been very successful at reconstructing biological networks, such as protein-ligand interaction, protein-protein interaction and gene regulatory networks.
Airola, Antti +4 more
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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 +5 more
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Synthesis, In Silico Studies, Antiprotozoal and Cytotoxic Activities of Quinoline‐Biphenyl Hybrids [PDF]
, 2020 This is the pre-peer reviewed version of the following article: Synthesis, In Silico Studies, Antiprotozoal and Cytotoxic Activities of Quinoline‐Biphenyl Hybrids, which has been published in final form at https://doi.org/10.1002/slct.201903835.
Carda, Miguel +6 more
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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 +3 more
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Predicting kinase inhibitor resistance: Physics-based and data-driven approaches. [PDF]
, 2019 Resistance to small molecule drugs often emerges in cancer cells, viruses, and bacteria as a result of the evolutionary pressure exerted by the therapy.
Aldeghi, M., de Groot, B., Gapsys, V.
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Graph–sequence attention and transformer for predicting drug–target affinity
RSC Advances, 2022 We proposed a novel model based on self-attention, called GSATDTA, to predict the binding affinity between drugs and targets. Experimental results show that our model outperforms the state-of-the-art methods on two independent datasets.
Xiangfeng Yan, Yong Liu
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Drug-target affinity prediction using applicability domain based on data density [PDF]
2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2021 In the pursuit of research and development of drug discovery, the computational prediction of the target affinity of a drug candidate is useful for screening compounds at an early stage and for verifying the binding potential to an unknown target. The chemogenomics-based method has attracted increased attention as it integrates information pertaining ...
Shunya Sugita, Masahito Ohue
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