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Chemoinformatics-Based Classification of Prohibited Substances Employed for Doping in Sport
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Evaluation of Gene Structure Prediction Programs
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An Introduction To Chemoinformatics
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Similarity Searching and Clustering of Chemical-Structure Databases Using Molecular Property Data
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Stochastic Generator of Chemical Structure. 1. Application to the Structure Elucidation of Large Molecules
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Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
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k Nearest Neighbors QSAR Modeling as a Variational Problem: Theory and Applications
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Similarity Searching of Chemical Databases Using Atom Environment Descriptors (MOLPRINT 2D): Evaluation of Performance
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Clustering of Large Databases of Compounds: Using the MDL “Keys” as Structural Descriptors
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Scaffold Hopping through Virtual Screening Using 2D and 3D Similarity Descriptors: Ranking, Voting, and Consensus Scoring
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A fair method for resetting the target in interrupted one-day cricket matches
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Tetrahydrogestrinone Is a Potent Androgen and Progestin
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An introduction to chemoinformatics
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Similarity methods in chemoinformatics
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Chemoinformatics‐Based Classification of Prohibited Substances Employed for Doping in Sport.
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Machine learning methods in chemoinformatics
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Classifying the World Anti-Doping Agency’s 2005 Prohibited List Using the Chemistry Development Kit Fingerprint
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Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds
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Predicting targets of compounds against neurological diseases using cheminformatic methodology
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Statistical discrimination of steroid profiles in doping control with support vector machines
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Informatics, Machine Learning and Computational Medicinal Chemistry
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