JP2019526792A5 - - Google Patents

Download PDF

Info

Publication number
JP2019526792A5
JP2019526792A5 JP2019508836A JP2019508836A JP2019526792A5 JP 2019526792 A5 JP2019526792 A5 JP 2019526792A5 JP 2019508836 A JP2019508836 A JP 2019508836A JP 2019508836 A JP2019508836 A JP 2019508836A JP 2019526792 A5 JP2019526792 A5 JP 2019526792A5
Authority
JP
Japan
Prior art keywords
thiol
ligand
target molecule
reaction mixture
reaction
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
JP2019508836A
Other languages
English (en)
Japanese (ja)
Other versions
JP7008688B2 (ja
JP2019526792A (ja
Filing date
Publication date
Priority claimed from GBGB1614152.5A external-priority patent/GB201614152D0/en
Application filed filed Critical
Publication of JP2019526792A publication Critical patent/JP2019526792A/ja
Publication of JP2019526792A5 publication Critical patent/JP2019526792A5/ja
Application granted granted Critical
Publication of JP7008688B2 publication Critical patent/JP7008688B2/ja
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

JP2019508836A 2016-08-18 2017-08-18 アッセイ Active JP7008688B2 (ja)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
GBGB1614152.5A GB201614152D0 (en) 2016-08-18 2016-08-18 Assay
GB1614152.5 2016-08-18
PCT/GB2017/052456 WO2018033753A2 (en) 2016-08-18 2017-08-18 Assay

Publications (3)

Publication Number Publication Date
JP2019526792A JP2019526792A (ja) 2019-09-19
JP2019526792A5 true JP2019526792A5 (enExample) 2020-09-24
JP7008688B2 JP7008688B2 (ja) 2022-02-10

Family

ID=57045465

Family Applications (1)

Application Number Title Priority Date Filing Date
JP2019508836A Active JP7008688B2 (ja) 2016-08-18 2017-08-18 アッセイ

Country Status (6)

Country Link
US (1) US11415579B2 (enExample)
EP (1) EP3500858B1 (enExample)
JP (1) JP7008688B2 (enExample)
ES (1) ES2972584T3 (enExample)
GB (1) GB201614152D0 (enExample)
WO (1) WO2018033753A2 (enExample)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112121052B (zh) * 2019-06-25 2024-10-22 复旦大学 丙烯酰基苯并氮杂䓬类化合物在制备防治血液肿瘤药物中的用途
CN113387831A (zh) * 2020-03-11 2021-09-14 苏州大学 酰胺类化合物及其在制备神经炎症抑制剂中的应用
JP2023548079A (ja) * 2020-10-28 2023-11-15 ジェネンテック, インコーポレイテッド 遊離チオール検出のための蛍光エルマンアッセイ
JP7667684B2 (ja) * 2021-04-06 2025-04-23 株式会社日立製作所 検体の分析前処理方法および検体前処理装置

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6335155B1 (en) 1998-06-26 2002-01-01 Sunesis Pharmaceuticals, Inc. Methods for rapidly identifying small organic molecule ligands for binding to biological target molecules
JP3836791B2 (ja) 2000-11-21 2006-10-25 サネシス ファーマシューティカルズ, インコーポレイテッド リガンドの迅速な同定のための拡張されたテザー化アプローチ
US6887667B2 (en) * 2000-12-28 2005-05-03 Alfred E. Mann Institute For Biomedical Engineering At The University Of Southern California Method and apparatus to identify small variations of biomolecules
US7672786B2 (en) * 2003-07-02 2010-03-02 Sergey Krylov Non-equilibrium capillary electrophoresis of equilibrium mixtures (NECEEM)—based methods for drug and diagnostic development
WO2005034840A2 (en) 2003-09-17 2005-04-21 Sunesis Pharmaceuticals, Inc. Identification of kinase inhibitors

Similar Documents

Publication Publication Date Title
JP2019526792A5 (enExample)
Pettinger et al. Lysine‐targeting covalent inhibitors
Mah et al. Drug discovery considerations in the development of covalent inhibitors
Goodwin et al. In silico predictions of blood-brain barrier penetration: considerations to “keep in mind”
Khamis et al. Comparative assessment of machine-learning scoring functions on PDBbind 2013
Ballester Selecting machine-learning scoring functions for structure-based virtual screening
Chojnacki et al. Polyubiquitin-photoactivatable crosslinking reagents for mapping ubiquitin interactome identify Rpn1 as a proteasome ubiquitin-associating subunit
JP2016527486A5 (enExample)
Troelsen et al. Library design strategies to accelerate fragment‐based drug discovery
JP2009521215A5 (enExample)
Cascales et al. Binding of glutamate to the umami receptor
Fornes et al. On the use of knowledge-based potentials for the evaluation of models of protein–protein, protein–DNA, and protein–RNA interactions
CN109313184A (zh) 用于治疗性抗体表征的基于阵列的肽文库
JP7008688B2 (ja) アッセイ
Knight et al. Impacting drug discovery projects with large-scale enumerations, machine learning strategies, and free-energy predictions
Liu et al. Advances in mass spectrometry-based epitope mapping of protein therapeutics
Reese et al. How far does a receptor influence vibrational properties of an odorant?
Yao et al. Parameter estimation and uncertainty quantification of rainfall-runoff models using data assimilation methods based on deep learning and local ensemble updates
Linnankoski et al. Passive oral drug absorption can be predicted more reliably by experimental than computational models—fact or myth
EP2795498B1 (en) Method of binding site and binding energy determination by mixed explicit solvent simulations
Arrad et al. Modeling the binary system Mn (NO3) 2–H2O with the extended universal quasichemical (UNIQUAC) model
US20190034580A1 (en) Methods for improved arrays or libraries using normalization strategies based on molecular structure
Wolter et al. Ligand photo-isomerization triggers conformational changes in iGluR2 ligand binding domain
JP2006030037A5 (enExample)
Habicht et al. Fitting error vs parameter performance─ how to choose reliable PC-SAFT pure-component parameters by physics-informed machine learning