Existing and Developing Approaches for QSAR Analysis of Mixtures

Abstract

This review is devoted to the critical analysis of advantages and disadvantages of existing mixture descriptors and their usage in various QSAR/QSPR tasks. We describe good practices for the QSAR modeling of mixtures, data sources for mixtures, a discussion of various mixture descriptors and their application, recommendations about proper external validation specific for mixture QSAR modeling, and future perspectives of this field. The biggest problem in QSAR of mixtures is the lack of reliable data about the mixtures' properties. Various mixture descriptors are used for the modeling of different endpoints. However, these descriptors have certain disadvantages, such as applicability only to 1 : 1 binary mixtures, and additive nature. The field of QSAR of mixtures is still under development, and existing efforts could be considered as a foundation for future approaches and studies. The usage of non-additive mixture descriptors, which are sensitive to interaction effects, in combination with best practices of QSAR model development (e.g., thorough data collection and curation, rigorous external validation, etc.) will significantly improve the quality of QSAR studies of mixtures.

QSAR Modeling: Where Have You Been? Where Are You Going To?

“…At the same time, although modern QSAR is successful in dealing with individual compounds, there are no mature QSAR methodologies that could be directly used to model properties of mixtures, reflecting the lack of robust, well-benchmarked data pertaining to such properties. 241 To date, only a few published QSAR studies of mixtures could be considered reliable. 242,243 An interested reader can find detailed descriptions of studies devoted to mixtures and QSAR modeling of their properties elsewhere.…”

Prediction of Surfactants’ Properties using Multiscale Molecular Modeling Tools: A Review

“…Regarding the HLB prediction, this work has put more emphasis on the need of larger databases and on the lack of QSPR models for cationic surfactants. It is also important to draw reader's attention to the fact that only properties of pure surfactants have been considered in the literature while industrial products are mostly mixtures of surfactants and the development of QSPR models for mixtures belongs to challenges of forthcoming years [137]. Finally, one can mention QSPR models for the prediction of the toxicity of surfactants which were developed on the basis of surfactant's properties such as the HLB, the hydrophobicity (log P), the CMC or the number of carbon atoms in the hydrophobic fragment [138,139].…”

Quimioinformática: Uma Introdução

“…Além dos trabalhos citados, outros estudos inovadores que compreendem a aplicação da quimioinformática para modelagem de peptídeos, 146,147 misturas de componentes [148][149][150] e nanopartículas 151 também foram descritos. As aplicações dos princípios de QSAR/QSPR são várias, de forma que diversas derivações da nomenclatura têm surgido na literatura, como QSRR (quantitative structure-(chromatographic) retention relationships), 152 QNAR (quantitative nanostructure-activity relationships), 153 QSTR (quantitative structure-toxicity relationships), 154 entre outros.…”