| Title |
Molecular Topology as Novel Strategy for Discovery of Drugs with Aβ Lowering and Anti-Aggregation Dual Activities for Alzheimer’s Disease
|
|---|---|
| Published in |
PLOS ONE, March 2014
|
| DOI | 10.1371/journal.pone.0092750 |
| Pubmed ID | |
| Authors |
Jun Wang, David Land, Kenjiro Ono, Jorge Galvez, Wei Zhao, Prashant Vempati, John W. Steele, Alice Cheng, Masahito Yamada, Samara Levine, Paolo Mazzola, Giulio M. Pasinetti |
| Abstract |
In this study, we demonstrate the use of Molecular topology (MT) in an Alzheimer's disease (AD) drug discovery program. MT uses and expands upon the principles governing the molecular connectivity theory of numerically characterizing molecular structures, in the present case, active anti-AD drugs/agents, using topological descriptors to build models. Topological characterization has been shown to embody sufficient molecular information to provide strong correlation to therapeutic efficacy. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 4% |
| Italy | 1 | 4% |
| Unknown | 21 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 4 | 17% |
| Researcher | 4 | 17% |
| Student > Doctoral Student | 3 | 13% |
| Student > Ph. D. Student | 3 | 13% |
| Unspecified | 1 | 4% |
| Other | 2 | 9% |
| Unknown | 6 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 13% |
| Computer Science | 3 | 13% |
| Agricultural and Biological Sciences | 3 | 13% |
| Medicine and Dentistry | 2 | 9% |
| Nursing and Health Professions | 1 | 4% |
| Other | 5 | 22% |
| Unknown | 6 | 26% |