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  5. Pairwise Interaction Field Neural Networks For Drug Discovery
 
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Pairwise Interaction Field Neural Networks For Drug Discovery

Author(s)
Lusci, Alessandro  
Pollastri, Gianluca  
Uri
http://hdl.handle.net/10197/12407
Date Issued
2013-06
Date Available
2021-08-11T12:20:03Z
Abstract
Automatically mapping small, drug-like molecules into their biological activity is an open problem in chemioinformatics. Numerous approaches to solve the problem have been attempted, which typically rely on different machine learning tools and, critically, depend on the how a molecule is represented (be it as a one-dimensional string, a two-dimensional graph, its three-dimensional structure, or a feature vector of some kind). In fact arguably the most critical bottleneck in the process is how to encode the molecule in a way that is both informative and can be dealt with by the machine learning algorithms downstream. Recently we have introduced an algorithm which entirely does away with this complex, error-prone and time-consuming encoding step by automatically finding an optimal code for a molecule represented as a twodimensional graph. In this report we introduce a model which we have recently developed (Neural Network Pairwise Interaction Fields) to extend this same approach to molecules represented as their three-dimensional structures. We benchmark the algorithm on a number of public data sets. While our tests confirm that three-dimensional representations are generally less informative than two-dimensional ones (possibly because the former are generally the result of a prediction process, and as such contain noise), the algorithm we introduce compares well with the state of the art in 3D-based prediction, in spite of not requiring any prior knowledge about the domain, or prior encoding of the molecule.
Sponsorship
Irish Research Council for Science, Engineering and Technology
Science Foundation Ireland
Type of Material
Technical Report
Publisher
University College Dublin. School of Computer Science and Informatics
Series
UCD CSI Technical Reports
ucd-csi-2013-02
Copyright (Published Version)
2013 the Authors
Subjects

Chemioinformatics

Neural network pairwi...

Machine learning

Web versions
https://web.archive.org/web/20080226040105/http:/csiweb.ucd.ie/Research/TechnicalReports.html
Language
English
Status of Item
Not peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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ucd-csi-2013-02.pdf

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Owning collection
Computer Science and Informatics Technical Reports
Mapped collections
CASL Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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