Results 71 to 80 of about 2,729,950 (336)

Personalized Selumetinib Dosing in Pediatric Neurofibromatosis Type 1: Insights From a Pilot Therapeutic Drug Monitoring Study

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Objective To evaluate selumetinib exposure using therapeutic drug monitoring (TDM) in pediatric patients with neurofibromatosis type 1 (NF1) and plexiform neurofibromas (PN), assess interpatient pharmacokinetic variability, and explore the relationship between drug exposure, clinical response, and adverse effects.
Janka Kovács, Zoltán Köllő, Balázs Gellért Karvaly, Mahmoud Obeidat, Rita Nagy, Péter Hegyi, Brigitta Teutsch, Eszter Tuboly, Miklós Garami   +8 more
wiley   +1 more source

Drug-Target Interaction Prediction Based on Drug Fingerprint Information and Protein Sequence

Molecules, 2019
The identification of drug-target interactions (DTIs) is a critical step in drug development. Experimental methods that are based on clinical trials to discover DTIs are time-consuming, expensive, and challenging.
Yang Li, Yu-An Huang, Zhu-Hong You, Li-Ping Li, Zheng Wang   +4 more
doaj   +1 more source

AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders

BMC Bioinformatics, 2021
Background Drug–target interaction (DTI) plays a vital role in drug discovery. Identifying drug–target interactions related to wet-lab experiments are costly, laborious, and time-consuming.
Seyedeh Zahra Sajadi, Mohammad Ali Zare Chahooki, Sajjad Gharaghani, Karim Abbasi   +3 more
doaj   +1 more source

iNGNN-DTI: prediction of drug–target interaction with interpretable nested graph neural network and pretrained molecule models

Bioinform.
Motivation Drug–target interaction (DTI) prediction aims to identify interactions between drugs and protein targets. Deep learning can automatically learn discriminative features from drug and protein target representations for DTI prediction, but ...
Y. Sun, Yan Yi Li, C. K. Leung, Pingzhao Hu   +3 more
semanticscholar   +1 more source

Deep Learning-Based Modeling of Drug–Target Interaction Prediction Incorporating Binding Site Information of Proteins

Interdisciplinary Sciences Computational Life Sciences, 2023
Chemogenomics, also known as proteochemometrics, covers various computational methods for predicting interactions between related drugs and targets on large-scale data. Chemogenomics is used in the early stages of drug discovery to predict the off-target
Sofia D'souza, ·. K. V. Prema, ·. S. Balaji, Ronak Shah   +3 more
semanticscholar   +1 more source

Two Faces of NOTCH1 in Childhood Lymphoblastic T‐Cell Neoplasia: Prognostic Divergence of Mutational and Structural Aberrations

Pediatric Blood &Cancer, EarlyView.
ABSTRACT In pediatric patients, T‐cell lymphoblastic lymphoma (T‐LBL) survival exceeds 80%. Relapse remains associated with limited curative options. Frontline treatment is largely extrapolated from T‐cell acute lymphoblastic leukemia (T‐ALL) treatment, reflecting the ongoing debate, whether both entities represent distinct diseases or variants within ...
Marie C. Heider, Amelie Alfert, Marc Hotfilder, Birgit Burkardt, Marcel te Vrugt   +4 more
wiley   +1 more source

Drug repurposing and prediction of multiple interaction types via graph embedding

BMC Bioinformatics, 2023
Background Finding drugs that can interact with a specific target to induce a desired therapeutic outcome is key deliverable in drug discovery for targeted treatment.
E. Amiri Souri, A. Chenoweth, S. N. Karagiannis, S. Tsoka   +3 more
doaj   +1 more source

Identifying potential drug-target interactions based on ensemble deep learning

Frontiers in Aging Neuroscience, 2023
IntroductionDrug-target interaction prediction is one important step in drug research and development. Experimental methods are time consuming and laborious.MethodsIn this study, we developed a novel DTI prediction method called EnGDD by combining ...
Liqian Zhou, Yuzhuang Wang, Lihong Peng, Zejun Li, Xueming Luo   +4 more
doaj   +1 more source

Bayer AG v. Housey Pharmaceuticals: Protection for Biotechnological Research Tools under Section 271(g) Found Wanting [PDF]

, 2005
[Excerpt] Research tools, a subset of biotechnological inventions protected by process patents, are “tools that scientists use in the laboratory, including cell lines, monoclonal antibodies, reagents, animal models, growth factors, combinatorial ...
Barthalow, Matthew
core   +1 more source

SynSysNet:integration of experimental data on synaptic protein-protein interactions with drug-target relations [PDF]

, 2012
We created SynSysNet, available online at http://bioinformatics.charite.de/ synsysnet, to provide a platform that creates a comprehensive 4D network of synaptic interactions.
August B. Smit, Bayes, Björn O. Gohlke, Croning, De Las Rivas, Flicek, Grant, Hanson, Hase, Hecker, J. D. Armstrong, Jakob M. J. Bauer, Joachim von Eichborn, Kamburov, Kanehisa, Li, Martin H. Schaefer, Mathias Dunkel, Mathur, Michael D. R. Croning, Michael F. Hoffmann, Miguel A. Andrade-Navarro, Nicolas Le Novere, Pim van Nierop, Pirooznia, Robert Preissner, Rose, Ruano, Sarah C. Preissner, Schaefer, Schneeberger, Seth G. N. Grant, Smoot, Sorokina, Stelzl, UniProt Consortium, von Eichborn, von Mering, Whitcomb, WHO Expert Committee, Yuzaki, Zhang, Zhang   +42 more
core   +2 more sources