Current Issues in Molecular Biology, 2022 Drug-target interactions provide insight into the drug-side effects and drug repositioning. However, wet-lab biochemical experiments are time-consuming and labor-intensive, and are insufficient to meet the pressing demand for drug research and ...
Lei Deng +4 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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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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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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Binding mode analyses of NAP derivatives as mu opioid receptor selective ligands through docking studies and molecular dynamics simulation [PDF]
, 2017 Mu opioid receptor selective antagonists are highly desirable because of their utility as pharmacological probes for receptor characterization and functional studies.
Wang, Huiqun +2 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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High-throughput Binding Affinity Calculations at Extreme Scales [PDF]
, 2018 Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily ...
Balasubramanian, Vivek +7 more
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Target identification strategies in plant chemical biology [PDF]
, 2014 The current needs to understand gene function in plant biology increasingly require more dynamic and conditional approaches opposed to classic genetic strategies.
Dejonghe, Wim, Russinova, Eugenia
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VNARs: An Ancient and Unique Repertoire of Molecules That Deliver Small, Soluble, Stable and High Affinity Binders of Proteins [PDF]
, 2015 Peer reviewedPublisher ...
Clem +4 more
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Multilevel Attention Models for Drug Target Binding Affinity Prediction [PDF]
Neural Processing Letters, 2021 Drug-Target Binding Affinity (DTBA) prediction is one class of Drug-Target Interaction problem (DTI), where the focus is to predict the binding strength of a drug-target pair. Several machine learning approaches have been developed for this purpose. However, almost all rely on the use of increasingly sophisticated inputs to improve the obtained results
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