An Evaluation of a Substructure Search Screen System Based on Bond-Centered Fragments

1974 ◽  
Vol 14 (1) ◽  
pp. 44-48 ◽  
Author(s):  
George W. Adamson ◽  
Judith A. Bush ◽  
Alice H. W. McLure ◽  
Michael F. Lynch
1985 ◽  
Vol 10 (2) ◽  
pp. 79-86 ◽  
Author(s):  
Anne Costigan ◽  
Frances E. Wood ◽  
David Bawden

A comparative evaluation of three implementations of a large databank, the NIOSH Registry of Toxic Effects of Chem ical Substances, has been carried out. The three implementa tions are: a printed index, a text searching computer system, and a computerised chemical databank system, with substruc ture searching facilities. Seven test queries were used, with the aim of drawing conclusions of general relevance to chemical databank searching. The computer systems were shown to have advantages over printed indexes for several of the queries, including those involving an element of browsing. Substructure search facilities were especially advantageous. Aspects of indexing of data present, and the criteria for inclusion of types of data, were also highlighted.


2020 ◽  
Vol 51 (1) ◽  
pp. 470-473
Author(s):  
Motohiro Makiguchi ◽  
Hideaki Takada ◽  
Tohru Kawakami ◽  
Mutsumi Sasai
Keyword(s):  

2020 ◽  
Vol 36 (8) ◽  
pp. 2602-2604 ◽  
Author(s):  
Evangelos Karatzas ◽  
Juan Eiros Zamora ◽  
Emmanouil Athanasiadis ◽  
Dimitris Dellis ◽  
Zoe Cournia ◽  
...  

Abstract Summary ChemBioServer 2.0 is the advanced sequel of a web server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines. Availability and implementation http://chembioserver.vi-seem.eu.


1993 ◽  
Vol 49 (2) ◽  
pp. 192
Author(s):  
Y Murakami ◽  
T Hamada ◽  
M Yamaguchi ◽  
Y Mochizuki ◽  
Y Koishi ◽  
...  
Keyword(s):  

1984 ◽  
Vol 26 (5) ◽  
pp. 286-293
Author(s):  
Kenji OSAKI ◽  
Tsuneyuki HIGASHI
Keyword(s):  

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