TopP-S: Persistent homology-based multi-task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility-Reference-Cited by-同舟云学术

TopP-S: Persistent homology-based multi-task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility

Published:2018-04-06 Issue:20 Volume:39 Page:1444-1454
ISSN:0192-8651
Container-title:Journal of Computational Chemistry
language:en
Short-container-title:J. Comput. Chem.

Author:

Wu Kedi¹^ORCID,Zhao Zhixiong²,Wang Renxiao³,Wei Guo-Wei¹⁴⁵^ORCID

Affiliation:

1. Department of Mathematics; Michigan State University, East Lansing; Michigan 48824

2. School of Medicine; Foshan University; Foshan Guangdong 528000 People's Republic of China

3. State Key Laboratory of Bioorganic Chemistry; Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences; Shanghai 200032 People's Republic of China

4. Department of Electrical and Computer Engineering; Michigan State University; Michigan 48824

5. Department of Biochemistry and Molecular Biology; Michigan State University; Michigan 48824

Funder

NSF

MSU Center for Mathematical Molecular Biosciences Initiative

Publisher

Wiley

Subject

Computational Mathematics,General Chemistry

Reference65 articles.

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3. p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure

4. A New Substituent Constant, π, Derived from Partition Coefficients

5. Partition coefficients and their uses

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