go 6976 has been researched along with b 43 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 | 0 (0.00) | 29.6817 |
| 2010's | 3 (75.00) | 24.3611 |
| 2020's | 1 (25.00) | 2.80 |
| Authors | Studies |
|---|---|
| Augustin, M; Davies, SP; Gao, Y; Harvey, KJ; Kovelman, R; Patel, UA; Woodward, 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 go 6976 and b 43
| Article | Year |
|---|---|
A broad activity screen in support of a chemogenomic map for kinase signalling research and drug discovery.
Topics: Aurora Kinases; Cluster Analysis; Drug Design; Drug Discovery; Drug Evaluation, Preclinical; ErbB Receptors; Humans; Intracellular Signaling Peptides and Proteins; MAP Kinase Kinase 4; p38 Mitogen-Activated Protein Kinases; Protein Kinase Inhibitors; Protein Kinases; Protein Serine-Threonine Kinases; Protein-Tyrosine Kinases; Receptors, Vascular Endothelial Growth Factor; Recombinant Proteins; Reproducibility of Results; Signal Transduction; Small Molecule Libraries; Structure-Activity Relationship; Syk Kinase | 2013 |
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 |