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Extracting Knowledge from Recombinations of SMILES Representations

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Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops (AIAI 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 677))

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Abstract

The exploitation of all possible combinations of the non-common substructure of compounds using Simplified Molecular-Input Line-Entry System (SMILES) representations is an essential part in terms of accurate chemical information processing. SMILES is a widely used encoding for representing chemical compounds as strings of characters. In our paper, a novel approach, which treats the SMILES strings as a sequence of letters, numbers and symbols in order to extract meaningful knowledge, is presented. It identifies the common substructure between two given SMILES. For the non-common substructure, we extensively search all possible combinations of the string characters of all possible lengths. Finally, for all these character combinations, we accept only those that are chemically correct. So, our approach suggests all possible substructures that may be present for the non-common substructure between two compounds using the atoms that already exist in the initial non-common substructure. This approach can generate all possible fragments that could exist for a given non-common substructure while maintaining the common substructure and could be used in drug discovery and other chemical applications.

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Acknowledgement

This research was co-financed by the European Union and Greek national funds through the “Competitiveness, Entrepreneurship and Innovation” Operational Programme 2014–2020, under the Call “Support for regional excellence”; project title: “Intelligent Research Infrastructure for Shipping, Transport and Supply Chain - ENIRISST+”; MIS code: 5047041.

Author information

Authors and Affiliations

  1. Computer Engineering and Informatics Department, University of Patras, Patras, Greece

    Christos Didachos

  2. Department of Informatics, Ionian University, Corfu, Greece

    Andreas Kanavos

Authors
  1. Christos Didachos
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  2. Andreas Kanavos
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Corresponding author

Correspondence to Andreas Kanavos .

Editor information

Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  4. Hellenic Telecom Organization OTE, Athens, Greece

    Ioannis Chochliouros

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Didachos, C., Kanavos, A. (2023). Extracting Knowledge from Recombinations of SMILES Representations. In: Maglogiannis, I., Iliadis, L., Papaleonidas, A., Chochliouros, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 677. Springer, Cham. https://doi.org/10.1007/978-3-031-34171-7_26

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  • DOI: https://doi.org/10.1007/978-3-031-34171-7_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34170-0

  • Online ISBN: 978-3-031-34171-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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