High-throughput screening of chemicals as functional substitutes using structure-based classification models.

ABSTRACT: Identifying chemicals that provide a specific function within a product, yet have minimal impact on the human body or environment, is the goal of most formulation chemists and engineers practicing green chemistry. We present a methodology to identify potential chemical functional substitutes from large libraries of chemicals using machine learning based models. We collect and analyze publicly available information on the function of chemicals in consumer products or industrial processes to identify a suite of harmonized function categories suitable for modeling. We use structural and physicochemical descriptors for these chemicals to build 41 quantitative structure-use relationship (QSUR) models for harmonized function categories using random forest classification. We apply these models to

SUBMITTER: Phillips KA 

PROVIDER: S-EPMC6260937 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature