data.md

@@ -2,12 +2,18 @@
 
 The CC capitalizes on many data sources. The following is an extensive list of resources that are worth considering in current and future versions of the CC. Inside each CC level, I list the resources in **alphabetical order**.
 
+* [X] Already in the CC.
+* [ ] Not yet in the CC.
+
 ## Observational data resources
 
 ### `A` Chemistry
 
 * [ ] ChemoPy [[paper](https://academic.oup.com/bioinformatics/article/29/8/1092/233093) [code]( http://code.google.com/p/pychem/downloads/list)]
  * A small chemoinformatics library focused on physicochemical properties and some fingerprints.
+* [ ] ClassyFire [[paper](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-016-0174-y) [code](https://bitbucket.org/wishartlab/classyfire_api)]
+ * Automatic classification of chemical compounds
+ * Ontology / taxonomy
 * [ ] DeepChem [[web](http://deepchem.io) [code](https://deepchem.io/docs/deepchem.html)]
  * A powerful deep learning chemoinformatics library, containing a large number of featurizers.
  * Among the interesting featurizers, there are the PDB-crystal embeddings, which should, in principle, enable connectivity between crystals and small molecules.
@@ -22,6 +28,8 @@ The CC capitalizes on many data sources. The following is an extensive list of r
 * [X] RDKIT [[paper]() [code](https://www.rdkit.org/)]
  * The standard library for chemoinformatics in `python`.
  * Calculates several fingerprints and also does 3D conformational sampling.
+* [X] Silicos-IT [[web](http://silicos-it.be.s3-website-eu-west-1.amazonaws.com/) [code](http://silicos-it.be.s3-website-eu-west-1.amazonaws.com/software/biscu-it/biscu-it.html)]
+ * It has implementations for the [chemical beauty](https://www.ncbi.nlm.nih.gov/pubmed/22270643) score (QED).
 
 ### `B` Targets