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Mining Statistically Significant Molecular Substructures for Efficient Molecular Classification

Published:2009-10-28 Issue:11 Volume:49 Page:2537-2550
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Ranu Sayan¹,Singh Ambuj K.¹

Affiliation:

1. Department of Computer Science, University of California, Santa Barbara, California

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/ci900035z

Reference58 articles.

1. Searching Techniques for Databases of Two- and Three-Dimensional Chemical Structures

2. Molecular similarity: a key technique in molecular informatics

3. A Neural Network Based Virtual High Throughput Screening Test for the Prediction of CNS Activity

4. Predicting Physical−Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks

5. Naïve Bayes Classification Using 2D Pharmacophore Feature Triplet Vectors

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2. GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph Generation;Proceedings of The Web Conference 2020;2020-04-19

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4. Grasping frequent subgraph mining for bioinformatics applications;BioData Mining;2018-09-03

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