Demo Data from VLDB 2026 / reviewers in the wild / expert
Mime Kobayashi
dblp:69/2242
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2003
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › molecular informatics › cheminformatics
quantitative structure-activity relationship |
0.0 | 1 | 2003 | Molecular evaluation using in silico protein interaction profiles · Bioinform. 2003 |
Methods — techniques the papers use, named apart from their topics
molecular docking · 0.0clustering · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2003 | Molecular evaluation using in silico protein interaction profilesabstractMOTIVATION: To find a correlation between the activities and structures of molecules is one of the most important subjects for molecular evaluation study. Traditional quantitative structure-activity relationship (QSAR) methodologies represent those attempts using physicochemical descriptors. Creating a new molecular description factor based on the results of a computational docking study will add new dimensions to molecular evaluation. RESULTS: We propose a new molecular description factor analysis system called the Comparative Molecular Interaction Profile Analysis (CoMIPA) system in which the AutoDock program is used for docking evaluation of small molecule compound-protein complexes. Interaction energies are calculated, and the data sets obtained are called interaction profiles (IPFs). Using the IPF as a scoring indicator, the system could be a powerful tool to cluster the interacting properties between small molecules and bio macromolecules such as ligand-receptor bindings. Further development of the system will enable us to predict the adverse effects of a drug candidate. Yoshiharu Hayashi, Katsuyoshi Sakaguchi, Mime Kobayashi, Masaki Kobayashi, Yo Kikuchi, Eiichiro Ichiishi |
Bioinform. | 3 |