AFGen

AFGen is a program that takes as input a set of chemical compounds and generates their vector-space representation based on the set of fragment-based descriptors they contain. The descriptor space consists of graph fragments that can have three different types of topologies: paths (PF), acyclic subgraphs (AF), and arbitrary topology subgraphs (GF). This vector-based representation can be used for different tasks in cheminformatics including similarity search, virtual screening, and library design. These descriptors are quite effective in capturing the structural characteristics of chemical compounds. Experiments in the context of SVM-based classification and ranked-retrieval show that these descriptors consistently and statistically outperform previously developed schemes based on the widely used fingerprint- and Maccs keys-based descriptors, as well as recently introduced descriptors obtained by mining and analyzing the structure of the molecular graphs.


References in zbMATH (referenced in 21 articles )

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  1. Bacciu, Davide; Conte, Alessio; Grossi, Roberto; Landolfi, Francesco; Marino, Andrea: K-plex cover pooling for graph neural networks (2021)
  2. Ma, Guixiang; Ahmed, Nesreen K.; Willke, Theodore L.; Yu, Philip S.: Deep graph similarity learning: a survey (2021)
  3. Ma, Zheng; Xuan, Junyu; Wang, Yu Guang; Li, Ming; Liò, Pietro: Path integral based convolution and pooling for graph neural networks (2021)
  4. Ahmed, Ammar; Hassan, Zohair Raza; Shabbir, Mudassir: Interpretable multi-scale graph descriptors via structural compression (2020)
  5. Bacciu, Davide; Errica, Federico; Micheli, Alessio; Podda, Marco: A gentle introduction to deep learning for graphs (2020)
  6. Kriege, Nils M.; Neumann, Marion; Morris, Christopher; Kersting, Kristian; Mutzel, Petra: A unifying view of explicit and implicit feature maps of graph kernels (2019)
  7. Ghosh, Swarnendu; Das, Nibaran; Gonçalves, Teresa; Quaresma, Paulo; Kundu, Mahantapas: The journey of graph kernels through two decades (2018)
  8. Bai, Lu; Hancock, Edwin R.: Fast depth-based subgraph kernels for unattributed graphs (2016)
  9. Neumann, Marion; Garnett, Roman; Bauckhage, Christian; Kersting, Kristian: Propagation kernels: efficient graph kernels from propagated information (2016)
  10. Gaüzère, Benoit; Grenier, Pierre-Anthony; Brun, Luc; Villemin, Didier: Treelet kernel incorporating cyclic, stereo and inter pattern information in chemoinformatics (2015)
  11. Bai, Lu; Hancock, Edwin R.: Depth-based complexity traces of graphs (2014)
  12. Schietgat, Leander; Ramon, Jan; Bruynooghe, Maurice: A polynomial-time maximum common subgraph algorithm for outerplanar graphs and its application to chemoinformatics (2013)
  13. Li, Geng; Semerci, Murat; Yener, Bülent; Zaki, Mohammed J.: Effective graph classification based on topological and label attributes (2012)
  14. Rohban, Mohammad Hossein; Rabiee, Hamid R.: Supervised neighborhood graph construction for semi-supervised classification (2012)
  15. Schietgat, Leander; Costa, Fabrizio; Ramon, Jan; De Raedt, Luc: Effective feature construction by maximum common subgraph sampling (2011)
  16. Chen, You-Shyang; Cheng, Ching-Hsue: Forecasting PGR of the financial industry using a rough sets classifier based on attribute-granularity (2010) ioport
  17. Thoma, Marisa; Cheng, Hong; Gretton, Arthur; Han, Jiawei; Kriegel, Hans-peter; Smola, Alex; Song, Le; Yu, Philip S.; Yan, Xifeng; Borgwardt, Karsten M.: Discriminative frequent subgraph mining with optimality guarantees (2010)
  18. Kamath, Chandrika: Scientific data mining. A practical perspective. (2009)
  19. Saigo, Hiroto; Nowozin, Sebastian; Kadowaki, Tadashi; Kudo, Taku; Tsuda, Koji: gBoost: a mathematical programming approach to graph classification and regression (2009)
  20. Wale, Nikil; Watson, Ian A.; Karypis, George: Comparison of descriptor spaces for chemical compound retrieval and classification (2008) ioport

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Further publications can be found at: http://glaros.dtc.umn.edu/gkhome/publications