Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition-Reference-Cited by-同舟云学术

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Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

Published:2018-01-10 Issue:1 Volume:58 Page:27-35
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Jaeger Sabrina¹^ORCID,Fulle Simone¹^ORCID,Turk Samo¹^ORCID

Affiliation:

1. BioMed X Innovation Center, Im Neuenheimer Feld 515, 69120 Heidelberg, Germany

Publisher

American Chemical Society (ACS)

Subject

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

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.7b00616

Reference44 articles.

1. Extended-Connectivity Fingerprints

2. Open-source platform to benchmark fingerprints for ligand-based virtual screening

3. Comparing structural fingerprints using a literature-based similarity benchmark

4. Heterogeneous Classifier Fusion for Ligand-Based Virtual Screening: Or, How Decision Making by Committee Can Be a Good Thing

5. DeepTox: Toxicity Prediction using Deep Learning

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