Improved understanding of aqueous solubility modeling through topological data analysis.

ABSTRACT: Topological data analysis is a family of recent mathematical techniques seeking to understand the 'shape' of data, and has been used to understand the structure of the descriptor space produced from a standard chemical informatics software from the point of view of solubility. We have used the mapper algorithm, a TDA method that creates low-dimensional representations of data, to create a network visualization of the solubility space. While descriptors with clear chemical implications are prominent features in this space, reflecting their importance to the chemical properties, an unexpected and interesting correlation between chlorine content and rings and their implication for solubility prediction is revealed. A parallel representation of the chemical space was generated using persistent

SUBMITTER: Pirashvili M 

PROVIDER: S-EPMC6755597 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature