Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression

scientific article published on 26 August 2016

Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression is …
instance of (P31):
scholarly articleQ13442814

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P8978DBLP publication IDjournals/jcisd/HorvathMVKLB16
P356DOI10.1021/ACS.JCIM.6B00359
P698PubMed publication ID27564682

P50authorJürgen BajorathQ18609784
Dragos HorvathQ39560704
Antonio de la Vega de LeónQ53345413
Gilles MarcouQ56800381
P2093author name stringAlexandre Varnek
Shilva Kayastha
P2860cites workSMILES, a chemical language and information system. 1. Introduction to methodology and encoding rulesQ28090714
Existing and Developing Approaches for QSAR Analysis of Mixtures.Q38914231
QSPR Approach to Predict Nonadditive Properties of Mixtures. Application to Bubble Point Temperatures of Binary Mixtures of LiquidsQ39540069
P433issue9
P921main subjectsupport vector machineQ282453
P304page(s)1631-1640
P577publication date2016-08-26
P1433published inJournal of Chemical Information and ModelingQ3007982
P1476titlePrediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression
P478volume56

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cites work (P2860)
Q57534282Automated support vector regression
Q90139000Evolving Concept of Activity Cliffs
Q39032777Matched Molecular Pair Analysis in Short: Algorithms, Applications and Limitations
Q90418732Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library
Q38689897Representation and identification of activity cliffs

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