Self organising hypothesis networks: a new approach for representing and structuring SAR knowledge
about
Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization processA k-Nearest Neighbours Approach Using Metabolism-related Fingerprints to Improve In Silico Metabolite Ranking.Origin of the TTC values for compounds that are genotoxic and/or carcinogenic and an approach for their re-evaluation.The Use of In Silico Models Within a Large Pharmaceutical Company.
P2860
Self organising hypothesis networks: a new approach for representing and structuring SAR knowledge
description
2014 nî lūn-bûn
@nan
2014 թուականին հրատարակուած գիտական յօդուած
@hyw
2014 թվականին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
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2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
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name
Self organising hypothesis net ...... and structuring SAR knowledge
@ast
Self organising hypothesis net ...... and structuring SAR knowledge
@en
Self organising hypothesis net ...... and structuring SAR knowledge
@nl
type
label
Self organising hypothesis net ...... and structuring SAR knowledge
@ast
Self organising hypothesis net ...... and structuring SAR knowledge
@en
Self organising hypothesis net ...... and structuring SAR knowledge
@nl
prefLabel
Self organising hypothesis net ...... and structuring SAR knowledge
@ast
Self organising hypothesis net ...... and structuring SAR knowledge
@en
Self organising hypothesis net ...... and structuring SAR knowledge
@nl
P2093
P2860
P356
P1476
Self organising hypothesis net ...... and structuring SAR knowledge
@en
P2093
Chris Barber
Edward Rosser
Jonathan D Vessey
Samuel J Webb
Stéphane Werner
Thierry Hanser
P2860
P2888
P356
10.1186/1758-2946-6-21
P407
P577
2014-01-01T00:00:00Z
P5875
P6179
1004986295