bromocriptine has been researched along with roxindole in 4 studies
Timeframe | Studies, this research(%) | All Research% |
---|---|---|
pre-1990 | 0 (0.00) | 18.7374 |
1990's | 0 (0.00) | 18.2507 |
2000's | 1 (25.00) | 29.6817 |
2010's | 2 (50.00) | 24.3611 |
2020's | 1 (25.00) | 2.80 |
Authors | Studies |
---|---|
Audinot, V; Boutin, JA; Cussac, D; Maiofiss, L; Millan, MJ; Newman-Tancredi, A | 1 |
Jadhav, A; Kerns, E; Nguyen, K; Shah, P; Sun, H; Xu, X; Yan, Z; Yu, KR | 1 |
Kabir, M; Kerns, E; Nguyen, K; Shah, P; Sun, H; Wang, Y; Xu, X; Yu, KR | 1 |
Kabir, M; Kerns, E; Neyra, J; Nguyen, K; Nguyễn, ÐT; Shah, P; Siramshetty, VB; Southall, N; Williams, J; Xu, X; Yu, KR | 1 |
4 other study(ies) available for bromocriptine and roxindole
Article | Year |
---|---|
Differential actions of antiparkinson agents at multiple classes of monoaminergic receptor. I. A multivariate analysis of the binding profiles of 14 drugs at 21 native and cloned human receptor subtypes.
Topics: Animals; Antiparkinson Agents; Binding Sites; Binding, Competitive; Cholinergic Antagonists; Cloning, Molecular; Cluster Analysis; Dopamine Agonists; Humans; Rats; Receptor, Muscarinic M1; Receptors, Adrenergic, alpha-1; Receptors, Adrenergic, alpha-2; Receptors, Adrenergic, beta; Receptors, Dopamine D1; Receptors, Dopamine D2; Receptors, Dopamine D3; Receptors, Histamine H1; Receptors, Muscarinic; Receptors, Neurotransmitter; Receptors, Serotonin | 2002 |
Highly predictive and interpretable models for PAMPA permeability.
Topics: Artificial Intelligence; Caco-2 Cells; Cell Membrane Permeability; Humans; Models, Biological; Organic Chemicals; Regression Analysis; Support Vector Machine | 2017 |
Predictive models of aqueous solubility of organic compounds built on A large dataset of high integrity.
Topics: Drug Discovery; Organic Chemicals; Pharmaceutical Preparations; Solubility | 2019 |
Retrospective assessment of rat liver microsomal stability at NCATS: data and QSAR models.
Topics: Animals; Computer Simulation; Databases, Factual; Drug Discovery; High-Throughput Screening Assays; Liver; Machine Learning; Male; Microsomes, Liver; National Center for Advancing Translational Sciences (U.S.); Pharmaceutical Preparations; Quantitative Structure-Activity Relationship; Rats; Rats, Sprague-Dawley; Retrospective Studies; United States | 2020 |