Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection
Abstract Despite being a central concept in cheminformatics, molecular similarity has so far been limited to the simultaneous comparison of only two molecules at a time and using one index, generally the Tanimoto coefficent. In a recent contribution we have not only introduced a complete mathematica...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-04-01
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| Series: | Journal of Cheminformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13321-021-00504-4 |
| _version_ | 1818385036183863296 |
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| author | Ramón Alain Miranda-Quintana Anita Rácz Dávid Bajusz Károly Héberger |
| author_facet | Ramón Alain Miranda-Quintana Anita Rácz Dávid Bajusz Károly Héberger |
| author_sort | Ramón Alain Miranda-Quintana |
| collection | DOAJ |
| description | Abstract Despite being a central concept in cheminformatics, molecular similarity has so far been limited to the simultaneous comparison of only two molecules at a time and using one index, generally the Tanimoto coefficent. In a recent contribution we have not only introduced a complete mathematical framework for extended similarity calculations, (i.e. comparisons of more than two molecules at a time) but defined a series of novel idices. Part 1 is a detailed analysis of the effects of various parameters on the similarity values calculated by the extended formulas. Their features were revealed by sum of ranking differences and ANOVA. Here, in addition to characterizing several important aspects of the newly introduced similarity metrics, we will highlight their applicability and utility in real-life scenarios using datasets with popular molecular fingerprints. Remarkably, for large datasets, the use of extended similarity measures provides an unprecedented speed-up over “traditional” pairwise similarity matrix calculations. We also provide illustrative examples of a more direct algorithm based on the extended Tanimoto similarity to select diverse compound sets, resulting in much higher levels of diversity than traditional approaches. We discuss the inner and outer consistency of our indices, which are key in practical applications, showing whether the n-ary and binary indices rank the data in the same way. We demonstrate the use of the new n-ary similarity metrics on t-distributed stochastic neighbor embedding (t-SNE) plots of datasets of varying diversity, or corresponding to ligands of different pharmaceutical targets, which show that our indices provide a better measure of set compactness than standard binary measures. We also present a conceptual example of the applicability of our indices in agglomerative hierarchical algorithms. The Python code for calculating the extended similarity metrics is freely available at: https://github.com/ramirandaq/MultipleComparisons |
| first_indexed | 2024-12-14T03:31:46Z |
| format | Article |
| id | doaj.art-e9491839e2b243a0b9befcd02d9e542e |
| institution | Directory Open Access Journal |
| issn | 1758-2946 |
| language | English |
| last_indexed | 2024-12-14T03:31:46Z |
| publishDate | 2021-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj.art-e9491839e2b243a0b9befcd02d9e542e2022-12-21T23:18:45ZengBMCJournal of Cheminformatics1758-29462021-04-0113111610.1186/s13321-021-00504-4Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selectionRamón Alain Miranda-Quintana0Anita Rácz1Dávid Bajusz2Károly Héberger3Department of Chemistry, University of FloridaPlasma Chemistry Research Group, Research Centre for Natural SciencesMedicinal Chemistry Research Group, Research Centre for Natural SciencesPlasma Chemistry Research Group, Research Centre for Natural SciencesAbstract Despite being a central concept in cheminformatics, molecular similarity has so far been limited to the simultaneous comparison of only two molecules at a time and using one index, generally the Tanimoto coefficent. In a recent contribution we have not only introduced a complete mathematical framework for extended similarity calculations, (i.e. comparisons of more than two molecules at a time) but defined a series of novel idices. Part 1 is a detailed analysis of the effects of various parameters on the similarity values calculated by the extended formulas. Their features were revealed by sum of ranking differences and ANOVA. Here, in addition to characterizing several important aspects of the newly introduced similarity metrics, we will highlight their applicability and utility in real-life scenarios using datasets with popular molecular fingerprints. Remarkably, for large datasets, the use of extended similarity measures provides an unprecedented speed-up over “traditional” pairwise similarity matrix calculations. We also provide illustrative examples of a more direct algorithm based on the extended Tanimoto similarity to select diverse compound sets, resulting in much higher levels of diversity than traditional approaches. We discuss the inner and outer consistency of our indices, which are key in practical applications, showing whether the n-ary and binary indices rank the data in the same way. We demonstrate the use of the new n-ary similarity metrics on t-distributed stochastic neighbor embedding (t-SNE) plots of datasets of varying diversity, or corresponding to ligands of different pharmaceutical targets, which show that our indices provide a better measure of set compactness than standard binary measures. We also present a conceptual example of the applicability of our indices in agglomerative hierarchical algorithms. The Python code for calculating the extended similarity metrics is freely available at: https://github.com/ramirandaq/MultipleComparisonshttps://doi.org/10.1186/s13321-021-00504-4Multiple comparisonsComputational complexityScalingRankingsExtended similarity indicesConsistency |
| spellingShingle | Ramón Alain Miranda-Quintana Anita Rácz Dávid Bajusz Károly Héberger Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection Journal of Cheminformatics Multiple comparisons Computational complexity Scaling Rankings Extended similarity indices Consistency |
| title | Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection |
| title_full | Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection |
| title_fullStr | Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection |
| title_full_unstemmed | Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection |
| title_short | Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection |
| title_sort | extended similarity indices the benefits of comparing more than two objects simultaneously part 2 speed consistency diversity selection |
| topic | Multiple comparisons Computational complexity Scaling Rankings Extended similarity indices Consistency |
| url | https://doi.org/10.1186/s13321-021-00504-4 |
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