Automated Discovery of Active Motifs in Three Dimensional Molecules

Xiong Wang; Jason T.L. Wang; Dennis Shasha; Bruce Shapiro; Sitaram Dikshitulu; Isidore Rigoutsos; Kaizhong Zhang

Automated Discovery of Active Motifs in Three Dimensional Molecules

Xiong Wang, Jason T.L. Wang, Dennis Shasha, Bruce Shapiro, Sitaram Dikshitulu, Isidore Rigoutsos, Kaizhong Zhang

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

17 Scopus citations

Abstract

In this paper we present a method for discovering approximately common motifs (also known as active motifs) in three dimensional (3D) molecules. Each node in a molecule is represented by a 3D point in the Euclidean Space and each edge is represented by an undirected line segment connecting two nodes in the molecule. Motifs are rigid substructures which may occur in a molecule after allowing for an arbitrary number of rotations and translations as well as a small number (specified by the user) of node insert/delete operations in the motifs or the molecule. (We call this "approximate occurrence.") The proposed method combines the geometric hashing technique and block detection algorithms for undirected graphs. To demonstrate the utility of our algorithms, we discuss their applications to classifying three families of molecules pertaining to antibacterial sulfa drugs, anti-anxiety agents (benzodiazepines) and antiadrenergic agents (β receptors). Experimental results indicate the good performance of our algorithms and the high quality of the discovered motifs.

Original language	English (US)
Title of host publication	Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997
Editors	David Heckerman, Heikki Mannila, Daryl Pregibon, Ramasamy Uthurusamy
Publisher	AAAI press
Pages	89-95
Number of pages	7
ISBN (Electronic)	1577350278, 9781577350279
State	Published - 1997
Event	3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997 - Newport Beach, United States Duration: Aug 14 1997 → Aug 17 1997

Publication series

Name	Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997

Conference

Conference	3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997
Country/Territory	United States
City	Newport Beach
Period	8/14/97 → 8/17/97

All Science Journal Classification (ASJC) codes

Software
Computer Science Applications

Cite this

Wang, X., Wang, J. T. L., Shasha, D., Shapiro, B., Dikshitulu, S., Rigoutsos, I., & Zhang, K. (1997). Automated Discovery of Active Motifs in Three Dimensional Molecules. In D. Heckerman, H. Mannila, D. Pregibon, & R. Uthurusamy (Eds.), Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997 (pp. 89-95). (Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997). AAAI press.

Wang, Xiong ; Wang, Jason T.L. ; Shasha, Dennis et al. / Automated Discovery of Active Motifs in Three Dimensional Molecules. Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997. editor / David Heckerman ; Heikki Mannila ; Daryl Pregibon ; Ramasamy Uthurusamy. AAAI press, 1997. pp. 89-95 (Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997).

@inproceedings{47e050a0053541ceaa55e0862ae47023,

title = "Automated Discovery of Active Motifs in Three Dimensional Molecules",

abstract = "In this paper we present a method for discovering approximately common motifs (also known as active motifs) in three dimensional (3D) molecules. Each node in a molecule is represented by a 3D point in the Euclidean Space and each edge is represented by an undirected line segment connecting two nodes in the molecule. Motifs are rigid substructures which may occur in a molecule after allowing for an arbitrary number of rotations and translations as well as a small number (specified by the user) of node insert/delete operations in the motifs or the molecule. (We call this {"}approximate occurrence.{"}) The proposed method combines the geometric hashing technique and block detection algorithms for undirected graphs. To demonstrate the utility of our algorithms, we discuss their applications to classifying three families of molecules pertaining to antibacterial sulfa drugs, anti-anxiety agents (benzodiazepines) and antiadrenergic agents (β receptors). Experimental results indicate the good performance of our algorithms and the high quality of the discovered motifs.",

author = "Xiong Wang and Wang, {Jason T.L.} and Dennis Shasha and Bruce Shapiro and Sitaram Dikshitulu and Isidore Rigoutsos and Kaizhong Zhang",

note = "Publisher Copyright: Copyright {\textcopyright} 1997, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.; 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997 ; Conference date: 14-08-1997 Through 17-08-1997",

year = "1997",

language = "English (US)",

series = "Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997",

publisher = "AAAI press",

pages = "89--95",

editor = "David Heckerman and Heikki Mannila and Daryl Pregibon and Ramasamy Uthurusamy",

booktitle = "Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997",

}

Wang, X, Wang, JTL, Shasha, D, Shapiro, B, Dikshitulu, S, Rigoutsos, I & Zhang, K 1997, Automated Discovery of Active Motifs in Three Dimensional Molecules. in D Heckerman, H Mannila, D Pregibon & R Uthurusamy (eds), Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997. Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997, AAAI press, pp. 89-95, 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997, Newport Beach, United States, 8/14/97.

Automated Discovery of Active Motifs in Three Dimensional Molecules. / Wang, Xiong; Wang, Jason T.L.; Shasha, Dennis et al.
Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997. ed. / David Heckerman; Heikki Mannila; Daryl Pregibon; Ramasamy Uthurusamy. AAAI press, 1997. p. 89-95 (Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Automated Discovery of Active Motifs in Three Dimensional Molecules

AU - Wang, Xiong

AU - Wang, Jason T.L.

AU - Shasha, Dennis

AU - Shapiro, Bruce

AU - Dikshitulu, Sitaram

AU - Rigoutsos, Isidore

AU - Zhang, Kaizhong

PY - 1997

Y1 - 1997

N2 - In this paper we present a method for discovering approximately common motifs (also known as active motifs) in three dimensional (3D) molecules. Each node in a molecule is represented by a 3D point in the Euclidean Space and each edge is represented by an undirected line segment connecting two nodes in the molecule. Motifs are rigid substructures which may occur in a molecule after allowing for an arbitrary number of rotations and translations as well as a small number (specified by the user) of node insert/delete operations in the motifs or the molecule. (We call this "approximate occurrence.") The proposed method combines the geometric hashing technique and block detection algorithms for undirected graphs. To demonstrate the utility of our algorithms, we discuss their applications to classifying three families of molecules pertaining to antibacterial sulfa drugs, anti-anxiety agents (benzodiazepines) and antiadrenergic agents (β receptors). Experimental results indicate the good performance of our algorithms and the high quality of the discovered motifs.

AB - In this paper we present a method for discovering approximately common motifs (also known as active motifs) in three dimensional (3D) molecules. Each node in a molecule is represented by a 3D point in the Euclidean Space and each edge is represented by an undirected line segment connecting two nodes in the molecule. Motifs are rigid substructures which may occur in a molecule after allowing for an arbitrary number of rotations and translations as well as a small number (specified by the user) of node insert/delete operations in the motifs or the molecule. (We call this "approximate occurrence.") The proposed method combines the geometric hashing technique and block detection algorithms for undirected graphs. To demonstrate the utility of our algorithms, we discuss their applications to classifying three families of molecules pertaining to antibacterial sulfa drugs, anti-anxiety agents (benzodiazepines) and antiadrenergic agents (β receptors). Experimental results indicate the good performance of our algorithms and the high quality of the discovered motifs.

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M3 - Conference contribution

AN - SCOPUS:0002621677

T3 - Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997

SP - 89

EP - 95

BT - Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997

A2 - Heckerman, David

A2 - Mannila, Heikki

A2 - Pregibon, Daryl

A2 - Uthurusamy, Ramasamy

PB - AAAI press

T2 - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997

Y2 - 14 August 1997 through 17 August 1997

ER -

Wang X, Wang JTL, Shasha D, Shapiro B, Dikshitulu S, Rigoutsos I et al. Automated Discovery of Active Motifs in Three Dimensional Molecules. In Heckerman D, Mannila H, Pregibon D, Uthurusamy R, editors, Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997. AAAI press. 1997. p. 89-95. (Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997).

Automated Discovery of Active Motifs in Three Dimensional Molecules

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All Science Journal Classification (ASJC) codes

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