Jul
30
CTR #6: Ring counts in a SMILES file
The sixth solution to CTR problems is one that uses a SSSR algorithm to count the number of rings in SMILES entries.
Jul
30
CTR #5: Convert a SMILES string to canonical SMILES
The CDK SmilesGenerator generates a canonical SMILES by default, so that one can simply parse the SMILES and generate a SMILES again to convert a SMILES string to a canonical SMILES:
import org.openscience.cdk.smiles.SmilesGenerator; import org.openscience.cdk.smiles.SmilesParser; import org.openscience.cdk.silent.SilentChemObjectBuilder; parser = new SmilesParser( SilentChemObjectBuilder.getInstance() ); generator = new SmilesGenerator(); smi = [ "CN2C(=O)N(C)C(=O)C1=C2N=CN1C", "CN1C=NC2=C1C(=
import org.openscience.cdk.smiles.SmilesGenerator; import org.openscience.cdk.smiles.SmilesParser; import org.openscience.cdk.silent.SilentChemObjectBuilder; parser = new SmilesParser( SilentChemObjectBuilder.getInstance() ); generator = new SmilesGenerator(); smi = [ "CN2C(=O)N(C)C(=O)C1=C2N=CN1C", "CN1C=NC2=C1C(=
Jul
30
CTR #4: Working with SD tag data
Next in the Chemistry Toolkit Rosetta CDK/Groovy solutions series is how to work with SD tag data. This CTR problem asks to scan the content of a number of SD fields, process them, and summarize them in a new field.
Jul
9
JChemInf volumes as single PDFs
One of the advantages of a print journal is that you are effectively forced to look at papers which may not have received your attention in the first place. Online journals do not provide such functionality, and you're stuck with the table of contents, and never see that cool figure from that paper with the boring title.
Of course, the problem is artificial. We have pdftk and we can make PDF of issues, or in the present example, of complete volumes. Handy, I'd say.
Of course, the problem is artificial. We have pdftk and we can make PDF of issues, or in the present example, of complete volumes. Handy, I'd say.
Jul
7
Visualizing SPARQL end point results with d3.js (but not yet for #WikiPathways)
SPARQL and RDF are very quickly becoming the (Open) standard for linking and accessing database works. Readers of my blog I have been searching the corners of what can and cannot be achieved with this for some time now.
Triggered by some nice visualization work at the BioHackathon on ChEMBL content, I picked up visualization of RDF data (see this 2010 post where I asked people to visualize data using SPARQL).
Triggered by some nice visualization work at the BioHackathon on ChEMBL content, I picked up visualization of RDF data (see this 2010 post where I asked people to visualize data using SPARQL).
