Improved understanding of aqueous solubility modeling through topological data analysis

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...

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Bibliographic Details
Main Authors: Mariam Pirashvili, Lee Steinberg, Francisco Belchi Guillamon, Mahesan Niranjan, Jeremy G. Frey, Jacek Brodzki
Format: Article
Language:English
Published: BMC 2018-11-01
Series:Journal of Cheminformatics
Subjects:
Solubility
Chemical space
Mapper
Persistent homology
Online Access:http://link.springer.com/article/10.1186/s13321-018-0308-5

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http://link.springer.com/article/10.1186/s13321-018-0308-5

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