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Raymond, John W.; Willett, Peter

doi:10.1023/a:1021271615909

Cited by 340 publications

(119 citation statements)

References 63 publications

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“…A computational method that converts the problem to be solved into a graph characterized by the character of the objects, such the atoms of a molecule or molecules in a database, and the distances between them [37]. Note: Examples are the Ullman algorithm used in substructure searching [38] or the detection of a pharmacophore or a maximum common substructure (MCS) within a set of molecules using the BronKerbosch algorithm.…”

Section: Graph-based Methodsmentioning

confidence: 99%

Glossary of terms used in computational drug design, part II (IUPAC Recommendations 2015)

Martin

Abagyan

Ferenczy

et al. 2016

Pure and Applied Chemistry

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Abstract:Computational drug design is a rapidly changing field that plays an increasingly important role in medicinal chemistry. Since the publication of the first glossary in 1997, substantial changes have occurred in both medicinal chemistry and computational drug design. This has resulted in the use of many new terms and the consequent necessity to update the previous glossary. For this purpose a Working Party of eight experts was assembled. They produced explanatory definitions of more than 150 new and revised terms.

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Section: Graph-based Methodsmentioning

confidence: 99%

Glossary of terms used in computational drug design, part II (IUPAC Recommendations 2015)

Martin

Abagyan

Ferenczy

et al. 2016

Pure and Applied Chemistry

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“…An alternative approach to MCS is to reduce the problem to finding a maximum clique in an association graph [2,15,24,40]. An association graph (or compatibility graph, or weak modular product) of two graphs G and H is an undirected graph…”

Section: Reformulation Of Mcs To a Maximum Clique Problemmentioning

confidence: 99%

“…In this paper, we focus on the connectedness constraint, which occurs in many applications [16,22,40,54]. Adding the connectedness requirement makes certain special cases solvable in polynomial time, including outerplanar graphs of bounded degree [1] and trees [14], but the general case remains NP-hard.…”

Section: Finding Maximum Common Connected Subgraphsmentioning

confidence: 99%

“…Maximum common subgraph problems arise in biology and chemistry [16,20,40], in computer vision [7,9], in the analysis of source code [12], binary programs [19], and circuit designs [9], in character recognition problems [27], and in many other domains [49], both directly and as a way of measuring the similarity or difference between two graphs [5,18,23]. We illustrate two variants of this problem in Fig.…”

Section: Introductionmentioning

confidence: 99%

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Clique and Constraint Models for Maximum Common (Connected) Subgraph Problems

McCreesh¹,

Ndiaye²,

Prosser³

et al. 2016

Lecture Notes in Computer Science

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Abstract. The maximum common subgraph problem is to find the largest subgraph common to two given graphs. This problem can be solved either by constraint-based search, or by reduction to the maximum clique problem. We evaluate these two models using modern algorithms, and see that the best choice depends mainly upon whether the graphs have labelled edges. We also study a variant of this problem where the subgraph is required to be connected. We introduce a filtering algorithm for this property and show that it may be combined with a restricted branching technique for the constraint-based approach. We show how to implement a similar branching technique in clique-inspired algorithms. Finally, we experimentally compare approaches for the connected version, and see again that the best choice depends on whether graphs have labels.

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“…Due to the relative complexity and inefficiency of computational algorithms to search for an MCS, [41] this approach, however, is rarely used to perform a similarity search [42] or to cluster chemical databases. [43] Another type of graph-based similarity measure is that of graph kernels which assign to each pair of graphs a positive real number characterizing similarity.…”

Section: Chemical Similarity As a Metric Of Chemical Spacementioning

confidence: 99%

Chemoinformatics as a Theoretical Chemistry Discipline

Varnek

Baskin

2011

Molecular Informatics

101

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Here, chemoinformatics is considered as a theoretical chemistry discipline complementary to quantum chemistry and force-field molecular modeling. These three fields are compared with respect to molecular representation, inference mechanisms, basic concepts and application areas. A chemical space, a fundamental concept of chemoinformatics, is considered with respect to complex relations between chemical objects (graphs or descriptor vectors). Statistical Learning Theory, one of the main mathematical approaches in structure-property modeling, is briefly reviewed. Links between chemoinformatics and its "sister" fields - machine learning, chemometrics and bioinformatics are discussed.

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Untitled

Cited by 340 publications

References 63 publications

Glossary of terms used in computational drug design, part II (IUPAC Recommendations 2015)

Glossary of terms used in computational drug design, part II (IUPAC Recommendations 2015)

Clique and Constraint Models for Maximum Common (Connected) Subgraph Problems

Chemoinformatics as a Theoretical Chemistry Discipline

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