tutorial

Indexing and mining topological patterns for drug discovery

Authors:

Ambuj K. SinghAuthors Info & Claims

EDBT '12: Proceedings of the 15th International Conference on Extending Database Technology

Pages 562 - 565

https://doi.org/10.1145/2247596.2247666

Published: 27 March 2012 Publication History

Abstract

Increased availability of large repositories of chemical compounds has created new challenges and opportunities for the application of data-mining and indexing techniques to problems in chemical informatics. The primary goal in analysis of molecular databases is to identify structural patterns that can predict biological activity. Two of the most popular approaches to representing molecular topologies are graphs and 3D geometries. As a result, the problem of indexing and mining structural patterns map to indexing and mining patterns from graph and 3D geometric databases.

In this tutorial, we will first introduce the problem of drug discovery and how computer science plays a critical role in that process. We will then proceed by introducing the problem of performing subgraph and similarity searches on large graph databases. Due to the NP-hardness of the problems, a number of heuristics have been designed in recent years and the tutorial will present an overview of those techniques. Next, we will introduce the problem of mining frequent subgraph patterns along with some of their limitations that ignited the interest in the problem of mining statistically significant subgraph patterns. After presenting an in-depth survey of the techniques on mining significant subgraph patterns, the tutorial will proceed towards the problem of analyzing 3D geometric structures of molecules. Finally, we will conclude by presenting two open computer science problems that can have a significant impact in the field of drug discovery.

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Cited By

Prateek AKhan AGoyal ARanu S(2020)Mining Top-k pairs of correlated subgraphs in a large networkProceedings of the VLDB Endowment10.14778/3397230.339724513:9(1511-1524)Online publication date: 26-Jun-2020
https://dl.acm.org/doi/10.14778/3397230.3397245
Vachery JArora ARanu SBhattacharya A(2019)RAQ: Relationship-Aware Graph Querying in Large NetworksThe World Wide Web Conference10.1145/3308558.3313448(1886-1896)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313448
Natarajan DRanu S(2018)ReslingKnowledge and Information Systems10.1007/s10115-017-1129-y54:1(123-149)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s10115-017-1129-y
Show More Cited By

Index Terms

Indexing and mining topological patterns for drug discovery
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Discrete space search
      2. Game tree search
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Published In

cover image ACM Other conferences

EDBT '12: Proceedings of the 15th International Conference on Extending Database Technology

March 2012

643 pages

ISBN:9781450307901

DOI:10.1145/2247596

Editors:
Elke Rundensteiner
Worcester Polytechnic Institute
,
Volker Markl
Technische Universität Berlin, Germany
,
Ioana Manolescu
INRIA, France
,
Sihem Amer-Yahia
QCRI, Doha, Qatar
,
Felix Naumann
Hasso Plattner Institute, Potsdam, Germany
,
Ismail Ari
Ozyegin University, Turkey

Copyright © 2012 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 March 2012

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EDBT '12

EDBT '12: 15th International Conference on Extending Database Technology

March 27 - 30, 2012

Berlin, Germany

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Cited By

Prateek AKhan AGoyal ARanu S(2020)Mining Top-k pairs of correlated subgraphs in a large networkProceedings of the VLDB Endowment10.14778/3397230.339724513:9(1511-1524)Online publication date: 26-Jun-2020
https://dl.acm.org/doi/10.14778/3397230.3397245
Vachery JArora ARanu SBhattacharya A(2019)RAQ: Relationship-Aware Graph Querying in Large NetworksThe World Wide Web Conference10.1145/3308558.3313448(1886-1896)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313448
Natarajan DRanu S(2018)ReslingKnowledge and Information Systems10.1007/s10115-017-1129-y54:1(123-149)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s10115-017-1129-y
Zhao P(2017)Similarity Search in Large-Scale Graph DatabasesHandbook of Big Data Technologies10.1007/978-3-319-49340-4_15(507-529)Online publication date: 26-Feb-2017
https://doi.org/10.1007/978-3-319-49340-4_15
Natarajan DRanu S(2016)A Scalable and Generic Framework to Mine Top-k Representative Subgraph Patterns2016 IEEE 16th International Conference on Data Mining (ICDM)10.1109/ICDM.2016.0048(370-379)Online publication date: Dec-2016
https://doi.org/10.1109/ICDM.2016.0048
Yun Peng Zhe Fan Byron Choi Jianliang Xu Bhowmick S(2015)Authenticated Subgraph Similarity Searchin Outsourced Graph DatabasesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2014.231681827:7(1838-1860)Online publication date: 1-Jul-2015
https://dl.acm.org/doi/10.1109/TKDE.2014.2316818
Dhifli WNguifo E(2015)Motif Discovery in Protein 3D‐Structures using Graph Mining TechniquesPattern Recognition in Computational Molecular Biology10.1002/9781119078845.ch10(165-189)Online publication date: 18-Dec-2015
https://doi.org/10.1002/9781119078845.ch10
Ranu SHoang MSingh ADyreson CLi FÖzsu M(2014)Answering top-k representative queries on graph databasesProceedings of the 2014 ACM SIGMOD International Conference on Management of Data10.1145/2588555.2610524(1163-1174)Online publication date: 18-Jun-2014
https://dl.acm.org/doi/10.1145/2588555.2610524

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