A QSTR-based expert system to predict sweetness of molecules

  • Cristian Rojas
  • , Roberto Todeschini
  • , Davide Ballabio
  • , Andrea Mauri
  • , Viviana Consonni
  • , Piercosimo Tripaldi
  • , Francesca Grisoni

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

This work describes a novel approach based on advanced molecular similarity to predict the sweetness of chemicals. The proposed Quantitative Structure-Taste Relationship (QSTR) model is an expert system developed keeping in mind the five principles defined by the Organization for Economic Co-operation and Development (OECD) for the validation of (Q)SARs. The 649 sweet and non-sweet molecules were described by both conformation-independent extended-connectivity fingerprints (ECFPs) and molecular descriptors. In particular, the molecular similarity in the ECFPs space showed a clear association with molecular taste and it was exploited for model development. Molecules laying in the subspaces where the taste assignation was more difficult were modeled trough a consensus between linear and local approaches (Partial Least Squares-Discriminant Analysis and N-nearest-neighbor classifier). The expert system, which was thoroughly validated through a Monte Carlo procedure and an external set, gave satisfactory results in comparison with the state-of-the-art models. Moreover, the QSTR model can be leveraged into a greater understanding of the relationship between molecular structure and sweetness, and into the design of novel sweeteners.

Original languageEnglish
Article number53
JournalFrontiers in Chemistry
Volume5
Issue numberJUL
DOIs
Publication statusPublished - 1 Jul 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2017 Rojas, Todeschini, Ballabio, Mauri, Consonni, Tripaldi and Grisoni.

Keywords

  • Classification
  • Expert system
  • Molecular descriptors
  • QSAR
  • Sweetness

Fingerprint

Dive into the research topics of 'A QSTR-based expert system to predict sweetness of molecules'. Together they form a unique fingerprint.

Cite this