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As a research laboratory, we are committed to exploiting the CC and to implement customary procedures that could be of use to computational drug discoverers. These applications are encapsulated as separate packages with seamless integration with the central CC resource.
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* Massive property prediction
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* [Similarity-based prediction](LINK-TO-PACKAGE): `targetmate`
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* [Similarity-based property (target) prediction](LINK-TO-PACKAGE): `targetmate`
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* This package derives from a work by David Amat (@damat) named TargetMate. We are currently evolving it into a simple kernel predictor that can take into account label correlation as well.
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* [Automated machine learning](LINK-TO-PACKAGE): `automl_cc`
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* [Automated machine learning](LINK-TO-PACKAGE): `automl_cc`
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* This is the work of Modesto Orozco (@morozco), who is currently using the AutoML TPOT library.
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* [DeepChem integration](LINK-TO-PACKAGE) `deepchem_cc`
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* The [DeepChem library](http://deepchem.io) is an outstanding chemoinformatics toolbox that incorporates a number of machine learning algorithms. Moreover, DeepChem contains [MoleculeNet](http://moleculenet.ai), a collection of benchmark sets related, among others, to biophysical properties and physiological outcomes of compounds.
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