Mining toxicity structural alerts from SMILES: A new way to derive structure activity relationships

Thomas Ferrari, Giuseppina Gini, Nazanin Golbamaki Bakhtyari, Emilio Benfenati

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Encouraged by recent legislations all over the world, aimed to protect human health and environment, in silico techniques have proved their ability to assess the toxicity of chemicals. However, they act often like a black-box, without giving a clear contribution to the scientific insight; such over-optimized methods may be beyond understanding, behaving more like competitors of human experts' knowledge, rather than assistants. In this work, a new Structure-Activity Relationship (SAR) approach is proposed to mine molecular fragments that act like structural alerts for biological activity. The entire process is designed to fit with human reasoning, not only to make its predictions more reliable, but also to enable a clear control by the user, in order to match customized requirements. Such an approach has been implemented and tested on the mutagenicity endpoint, showing marked prediction skills and, more interestingly, discovering much of the knowledge already collected in literature as well as new evidences. The achieved tool is a powerful instrument for both SAR knowledge discovery and for activity prediction on untested compounds.

Original languageEnglish
Title of host publicationIEEE SSCI 2011: Symposium Series on Computational Intelligence - CIDM 2011: 2011 IEEE Symposium on Computational Intelligence and Data Mining
Pages120-127
Number of pages8
DOIs
Publication statusPublished - 2011
EventSymposium Series on Computational Intelligence, IEEE SSCI2011 - 2011 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2011 - Paris, France
Duration: Apr 11 2011Apr 15 2011

Other

OtherSymposium Series on Computational Intelligence, IEEE SSCI2011 - 2011 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2011
Country/TerritoryFrance
CityParis
Period4/11/114/15/11

Keywords

  • fragments
  • knowledge discovery
  • mutagenicity
  • SMILES
  • structural alerts
  • Structure Activity Relationships

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Science Applications

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