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... | @@ -72,12 +72,12 @@ Finding the right connectivity strategy requires a deep understanding of the dat |
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For a thorough explanation of the connectivity strategies, please visit:
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For a thorough explanation of the connectivity strategies, please visit:
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* [Connectivity](connectivity)
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* [Connectivity](connectivity)
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## Customary drug discovery tasks
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## Exploitation
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We are currently most interested in making the CC resource available to everyone and to identify standard applications that are of use to computational drug discoverers. Below, we list some such applications:
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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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* [Library characterization](exploitation):
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* [Library characterization](LINK-TO-PACKAGE) `library_cc`:
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* Massive property prediction:
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* Massive property prediction:
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* [Similarity-based prediction](exploitation): This work emerges from a work by David Amat (@damat) named TargetMate. We are currenty evolving it into a simple kernel predictor that can take into account label correlation as well.
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* [Similarity-based prediction](LINK-TO-PACKAGE) `targetmate`: 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](exploitation): This is the work of Modesto Orozco (@morozco), who is currently using the AutoML TPOT library.
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* [Automated machine learning](LINK-TO-PACKAGE) `automl_cc`: This is the work of Modesto Orozco (@morozco), who is currently using the AutoML TPOT library.
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* [DeepChem integration](exploitation): The [DeepChem library](http://deepchem.io) is an outstanding chemoinformatics toolbox that incorporates a number of machine learning algorithms with seamless integration with `tensorflow` and similars. Moreover, DeepChem contains [MoleculeNet](http://moleculenet.ai), a collection of benchmark sets related, among others, to biophysical properties and physiological outcomes of compounds. Conveniently, DeepChem contains several `featurizers`, i.e. functions that are able to convert a compound structure to a vector format, typically representing their chemistry. We branch DeepChem to include `cc_featurizers` able to convert, in principle, *any* compound structure to the corresponding signature. We've started doing so with Type 2 signatures. This is the work of Martino Bertoni (@mbertoni). |
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* [DeepChem integration](LINK-TO-PACKAGE) `deepchem_cc`: 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. Conveniently, DeepChem contains several `featurizers`, i.e. functions that are able to convert a compound structure to a vector format, typically representing their chemistry. We branch DeepChem to include `cc_featurizers` able to convert, in principle, *any* compound structure to the corresponding signature. Martino Bertoni (@mbertoni) has started doing so with Type 2 signatures. |