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SMIREP: Predicting Chemical Activity from SMILES
Andreas Karwath
, Luc De Raedt
Cancer and Genomic Sciences
The Centre for Computational Biology
Research output
:
Contribution to journal
›
Article
›
peer-review
32
Citations (Scopus)
Overview
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Keyphrases
Estrogen Receptor
100%
Structure-activity Relationship
100%
Chemical Activity
100%
Highly Accurate
50%
Binding Activity
50%
Prediction Accuracy
50%
Small Sets
50%
Model Generation
50%
Large Set
50%
Predictive Models
50%
Machine Learning Techniques
50%
Environmental Fate
50%
Data Mining Techniques
50%
State-of-the-art Techniques
50%
Biodegradability
50%
Mutagenicity
50%
National Center
50%
Carcinogenic Potency
50%
Toxicological Research
50%
Alternative States
50%
Relationship Prediction
50%
Distributed Structure
50%
Rules of Interpretation
50%
SMILES Strings
50%
Rule Learner
50%
Environmental Protection Agency
50%
Fragment Generation
50%
Computational Chemists
50%
If-then Rules
50%
Computer Science
Predictive Model
100%
Machine Learning Technique
100%
Model Generation
100%
Predictive Accuracy
100%
Data Mining Technique
100%
Distributed Agency
100%
Pharmacology, Toxicology and Pharmaceutical Science
Estrogen Receptor
100%
Structure Activity Relationship
100%
Mutagenicity
50%