Applications of self-organizing neural networks in virtual screening and diversity selection.
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Selzer, Paul and Ertl, Peter (2006) Applications of self-organizing neural networks in virtual screening and diversity selection. Journal of Chemical Information and Modeling, 46 (6). pp. 2319-2323. ISSN 1549-9596
Abstract
Artificial neural networks provide a powerful technique for the analysis and modeling of nonlinear relationships between molecular structures and pharmacological activity. Many network types, including Kohonen and counterpropagation, also provide an intuitive method for the visual assessment of correspondence between the input and output data. This work shows how a combination of neural networks and radial distribution function molecular descriptors can be applied in various areas of industrial pharmaceutical research. These applications include the prediction of biological activity, the selection of screening candidates (cherry picking), and the extraction of representative subsets from large compound collections such as combinatorial libraries. The methods described have also been implemented as an easy-to-use Web tool, allowing chemists to perform interactive neural network experiments on the Novartis intranet.
| Item Type: | Article |
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| Additional Information: | archiving not formally supported by this publisher |
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| Date Deposited: | 14 Dec 2009 13:57 |
| Last Modified: | 31 Jan 2013 01:13 |
| URI: | https://oak.novartis.com/id/eprint/562 |