Results 41 to 50 of about 160,727 (166)
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
openaire +2 more sources
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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Virtual Affinity Fingerprints for Target Fishing: A New Application of Drug Profile Matching [PDF]
, 2013 We recently introduced Drug Profile Matching (DPM), a novel virtual affinity fingerprinting bioactivity prediction method. DPM is based on the docking profiles of ca.
Czobor, P. +6 more
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The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions [PDF]
, 2018 Accepted for publication in a future issue of Future Medicinal Chemistry.The research into the use of small molecules as drugs continues to be a key driver in the development of molecular databases, computer-aided drug design software and collaborative ...
Blomberg N +6 more
core +2 more sources