Efficient calculation of compound similarity based on maximum common subgraphs and its application to prediction of gene transcript levels
Properties of a chemical entity, both physical and biological, are related to its structure. Since compound similarity can be used to infer properties of novel compounds, in chemoinformatics much attention has been paid to ways of calculating structural similarity. A useful metric to capture the str...
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| Vydáno v: | International journal of bioinformatics research and applications Ročník 9; číslo 4; s. 407 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Switzerland
2013
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| Témata: | |
| ISSN: | 1744-5485 |
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| Abstract | Properties of a chemical entity, both physical and biological, are related to its structure. Since compound similarity can be used to infer properties of novel compounds, in chemoinformatics much attention has been paid to ways of calculating structural similarity. A useful metric to capture the structural similarity between compounds is the relative size of the Maximum Common Subgraph (MCS). The MCS is the largest substructure present in a pair of compounds, when represented as graphs. However, in practice it is difficult to employ such a metric, since calculation of the MCS becomes computationally intractable when it is large. We propose a novel algorithm that significantly reduces computation time for finding large MCSs, compared to a number of state-of-the-art approaches. The use of this algorithm is demonstrated in an application predicting the transcriptional response of breast cancer cell lines to different drug-like compounds, at a scale which is challenging for the most efficient MCS-algorithms to date. In this application 714 compounds were compared. |
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| AbstractList | Properties of a chemical entity, both physical and biological, are related to its structure. Since compound similarity can be used to infer properties of novel compounds, in chemoinformatics much attention has been paid to ways of calculating structural similarity. A useful metric to capture the structural similarity between compounds is the relative size of the Maximum Common Subgraph (MCS). The MCS is the largest substructure present in a pair of compounds, when represented as graphs. However, in practice it is difficult to employ such a metric, since calculation of the MCS becomes computationally intractable when it is large. We propose a novel algorithm that significantly reduces computation time for finding large MCSs, compared to a number of state-of-the-art approaches. The use of this algorithm is demonstrated in an application predicting the transcriptional response of breast cancer cell lines to different drug-like compounds, at a scale which is challenging for the most efficient MCS-algorithms to date. In this application 714 compounds were compared. Properties of a chemical entity, both physical and biological, are related to its structure. Since compound similarity can be used to infer properties of novel compounds, in chemoinformatics much attention has been paid to ways of calculating structural similarity. A useful metric to capture the structural similarity between compounds is the relative size of the Maximum Common Subgraph (MCS). The MCS is the largest substructure present in a pair of compounds, when represented as graphs. However, in practice it is difficult to employ such a metric, since calculation of the MCS becomes computationally intractable when it is large. We propose a novel algorithm that significantly reduces computation time for finding large MCSs, compared to a number of state-of-the-art approaches. The use of this algorithm is demonstrated in an application predicting the transcriptional response of breast cancer cell lines to different drug-like compounds, at a scale which is challenging for the most efficient MCS-algorithms to date. In this application 714 compounds were compared.Properties of a chemical entity, both physical and biological, are related to its structure. Since compound similarity can be used to infer properties of novel compounds, in chemoinformatics much attention has been paid to ways of calculating structural similarity. A useful metric to capture the structural similarity between compounds is the relative size of the Maximum Common Subgraph (MCS). The MCS is the largest substructure present in a pair of compounds, when represented as graphs. However, in practice it is difficult to employ such a metric, since calculation of the MCS becomes computationally intractable when it is large. We propose a novel algorithm that significantly reduces computation time for finding large MCSs, compared to a number of state-of-the-art approaches. The use of this algorithm is demonstrated in an application predicting the transcriptional response of breast cancer cell lines to different drug-like compounds, at a scale which is challenging for the most efficient MCS-algorithms to date. In this application 714 compounds were compared. |
| Author | Verheijen, Peter J T Reinders, Marcel J T Winterbach, Wynand Bender, Andreas Berlo, Rogier J P Van Groot, Marco J L De Ridder, Dick De |
| Author_xml | – sequence: 1 givenname: Rogier J P Van surname: Berlo fullname: Berlo, Rogier J P Van email: r.j.p.vanberlo@tudelft.nl organization: The Delft Bioinformatics Lab/Kluyver Centre for Genomics of Industrial Fermentation, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. r.j.p.vanberlo@tudelft.nl – sequence: 2 givenname: Wynand surname: Winterbach fullname: Winterbach, Wynand – sequence: 3 givenname: Marco J L De surname: Groot fullname: Groot, Marco J L De – sequence: 4 givenname: Andreas surname: Bender fullname: Bender, Andreas – sequence: 5 givenname: Peter J T surname: Verheijen fullname: Verheijen, Peter J T – sequence: 6 givenname: Marcel J T surname: Reinders fullname: Reinders, Marcel J T – sequence: 7 givenname: Dick De surname: Ridder fullname: Ridder, Dick De |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23797997$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1007_s42979_020_00431_5 crossref_primary_10_1016_j_molstruc_2021_130980 crossref_primary_10_1109_TKDE_2019_2922956 crossref_primary_10_1038_s41598_017_11508_2 crossref_primary_10_1038_s41598_018_34692_1 crossref_primary_10_1007_s10115_015_0874_z crossref_primary_10_1016_j_ymssp_2020_107144 crossref_primary_10_1007_s10115_015_0844_5 |
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| SubjectTerms | Algorithms Databases, Factual Drug Design Models, Molecular Pharmaceutical Preparations - chemistry Transcription, Genetic |
| Title | Efficient calculation of compound similarity based on maximum common subgraphs and its application to prediction of gene transcript levels |
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