Use of Quasi-SMILES and monte carlo optimization to develop quantitative feature property/activity relationships (QFPR/QFAR) for nanomaterials

Andrey A. Toropov, Robert Rallo, Alla P. Toropova

Research output: Contribution to journalArticlepeer-review

Abstract

The CORAL software (http://www.insilico.eu/coral) has been used to develop quantitative feature–property/activity relationships (QFPRs/QFARs) for the prediction of endpoints related to different categories of nanomaterials. In contrast to previous models built up by using CORAL from a representation of the molecular structure by using simplified molecular input-line entry system (SMILES), the current QFPR/QFARs are based on an integrated representation of acting conditions (i.e., a combination of physicochemical and/or biochemical factors) of nanomaterials via the so-called quasi-SMILES notation. In contrast to traditional quantitative structure – property/activity relationships (QSPRs/QSARs), the new models are able to provide new insight on the conditions of acting of substances (e.g., chemicals and nanomaterials) independently of their molecular structure. The development and validation of the QFPR/QFAR models was carried out following the OECD principles. The statistical quality of models developed from quasi-SMILES is acceptable, with values for the determination coefficient in the range of 0.70 to 0.85 for various endpoints of environmental and human health relevance. Perspectives of the QFPR/QFAR and their interaction and overlapping with traditional QSPR/QSAR are also discussed.

Original languageEnglish
Pages (from-to)1837-1844
Number of pages8
JournalCurrent Topics in Medicinal Chemistry
Volume15
Issue number18
Publication statusPublished - Jul 1 2015

Keywords

  • Monte Carlo method
  • Nanomaterial
  • QFPR/QFAR
  • QSPR/QSAR
  • Quasi-SMILES

ASJC Scopus subject areas

  • Drug Discovery

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