Mining High-Throughput Screening Data of Combinatorial Libraries: Development of a Filter to Distinguish Hits from Nonhits-Reference-Cited by-同舟云学术

Mining High-Throughput Screening Data of Combinatorial Libraries: Development of a Filter to Distinguish Hits from Nonhits

Published:2004-02-28 Issue:2 Volume:44 Page:626-634
ISSN:0095-2338
Container-title:Journal of Chemical Information and Computer Sciences
language:en
Short-container-title:J. Chem. Inf. Comput. Sci.

Author:

Teckentrup Andreas¹,Briem Hans¹,Gasteiger Johann¹

Affiliation:

1. Department of Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Strasse 65, D-88397 Biberach, Germany, and Computer-Chemie-Centrum, Institute for Organic Chemistry, University of Erlangen-Nürnberg, Nägelsbachstrasse 25, D-91052 Erlangen, Germany

Publisher

American Chemical Society (ACS)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/ci034223v

Reference22 articles.

1. Anzali S.; Gasteiger, J.; Holzgrabe, U.; Polanski, J. Sadowski, J.; Teckentrup, A.; Wagener, M. The Use of Self-Organizing Neural Networks in Drug Design. In3D QSAR in Drug Design; Kubinyi, H.; Folkers, G.; Martin, Y. C., Eds.; Kluwer/ESCOM: Dordrecht, Netherlands, 1998; Vol. 2, pp 273−299.

2. Self-organized formation of topologically correct feature maps

3. Self-Organization and Associative Memory

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