Learning Drug Functions from Chemical Structures with Convolutional Neural Networks and Random Forests-Reference-Cited by-同舟云学术

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Learning Drug Functions from Chemical Structures with Convolutional Neural Networks and Random Forests

Published:2019-09-13 Issue:10 Volume:59 Page:4438-4449
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Meyer Jesse G.^ORCID,Liu Shengchao^ORCID,Miller Ian J.^ORCID,Coon Joshua J.^ORCID,Gitter Anthony^ORCID

Funder

Wisconsin Alumni Research Foundation

U.S. National Library of Medicine

National Institute of General Medical Sciences

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

http://pubs.acs.org/doi/pdf/10.1021/acs.jcim.9b00236

Reference72 articles.

1. Navigating chemical space for biology and medicine

2. Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17

3. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1

4. Estimation of the size of drug-like chemical space based on GDB-17 data

5. Deep learning for computational chemistry

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