A dibenzooxepine that is 6,11-dihydrodibenzo[b,e]oxepine substituted by a 3-(dimethylamino)propylidene group at position 11. It is used as an antidepressant drug.
ChEBI ID: 4710
There is 1 compound belonging to this class, involving 2 study.
Member | Definition | Role |
---|---|---|
cis-doxepin |
Pre-1990 | 1990-2000 | 2001-2010 | 2011-2020 | Post-2020 |
---|---|---|---|---|
0 | 0 | 0 | 2 | 0 |
Article |
---|
A High-Throughput Screening Strategy to Identify Protein-Protein Interaction Inhibitors That Block the Fanconi Anemia DNA Repair Pathway.
Induction of the Fanconi anemia (FA) DNA repair pathway is a common mechanism by which tumors evolve resistance to DNA crosslinking chemotherapies. Proper execution of the FA pathway requires interaction between the FA complementation group M protein (FANCM) and the RecQ-mediated genome instability protein (RMI) complex, and mutations that disrupt FANCM/RMI interactions sensitize cells to DNA crosslinking agents. Inhibitors that block FANCM/RMI complex formation could be useful therapeutics for resensitizing tumors that have acquired chemotherapeutic resistance. To identify such inhibitors, we have developed and validated high-throughput fluorescence polarization and proximity assays that are sensitive to inhibitors that disrupt interactions between the RMI complex and its binding site on FANCM (a peptide referred to as MM2). A pilot screen of 74,807 small molecules was performed using the fluorescence polarization assay. Hits from the primary screen were further tested using the proximity assay, and an orthogonal proximity assay was used to assess inhibitor selectivity. Direct physical interaction between the RMI complex and the most selective inhibitor identified through the screening process was measured by surface plasmon resonance and isothermal titration calorimetry. Observation of direct binding by this small molecule validates the screening protocol. |
Predicting hERG activities of compounds from their 3D structures: development and evaluation of a global descriptors based QSAR model.
A QSAR based predictive model of hERG activity in terms of 'global descriptors' has been developed and evaluated. The QSAR was developed by training 77 compounds covering a wide range of activities and was validated based on an external 'test set' of 80 compounds using neural network method. Statistical parameters and examination of enrichment factor indicated the effectiveness of the present model. Randomization test demonstrated the robustness of the model and cross-validation test further validated the QSAR. Domain of applicability test indicated to the high degree of reliability of the predicted results. Satisfactory performance in classifying compounds into 'active' and 'inactive' groups was also obtained. The cases where the QSAR failed, the possible sources of errors have been discussed. |