Sunday, June 28, 2015

#metsoc2015 Converting SMILES annotation into InChIKey annotation

One of the questions I had in the hackathon today is about how to use the CDK to convert SMILES string into InChIs and InChIKeys (see doi:10.1186/1758-2946-5-14). So, here goes. This is the Groovy variant, though you can access the CDK just as well in other programming languages (Python, Java, JavaScript). We'll use the binary jar for CDK 1.5.10.  We can then run code, say test.groovy, using the CDK with:

groovy -cp cdk-1.5.10.jar test.groovy

With that out of the way, let's look at the code. Let's assume we start with a text file with one SMILES string on each line, say test.smi, then we parse this file with:

new File("test.smi").eachLine { line ->
  mol = parser.parseSmiles(line)

This already parses the SMILES string into a chemical graph. If we pass this to the generator to create an InChIKey, we may get an error, so we do an extra check:

gen = factory.getInChIGenerator(mol)
if (gen.getReturnStatus() == INCHI_RET.OKAY) {
  println gen.inchiKey;
} else {
  println "# error: " + gen.message

If we combine these two bits, we get a full test.groovy program:

import org.openscience.cdk.silent.*
import org.openscience.cdk.smiles.*
import org.openscience.cdk.inchi.*
import net.sf.jniinchi.INCHI_RET

parser = new SmilesParser(
factory = InChIGeneratorFactory.instance

new File("test.smi").eachLine { line ->
  mol = parser.parseSmiles(line)
  gen = factory.getInChIGenerator(mol)
  if (gen.getReturnStatus() == INCHI_RET.OKAY) {
    println gen.inchiKey;
  } else {
    println "# error: " + gen.message

Update: John May suggested an update, which I quite like. If the result is not 100% okay, but the InChI library gave a warning, it still yields an InChIKey which we can output, along with the warning message. For this, replace the if-else statement with this code:

if (gen.returnStatus == INCHI_RET.OKAY) {
  println gen.inchiKey;
} else if (gen.returnStatus == INCHI_RET.WARNING) {
  println gen.inchiKey + " # warning: " + gen.message;
} else {
  println "# error: " + gen.message


Sunday, June 07, 2015

CDK Literature #7

CC-BY-SA from WikiMedia.
Despite evidence that it does not make sense to aim for something, I did it again: I aimed at discussion some five CDK-citing papers each week. That was three weeks ago, and I don't really have time today either. But let me cover a few, so that I do not get even further behind.

Subset selection in QSAR modeling
We (intuitively) know that negative data is important for statistical pattern recognition and modelling. We also know that literature is not quite riddled with such data. This paper, however, studies the effect of using sets of inactive compounds in modelling and particularly the part about selecting which compounds should go into the training set. Like with the positive compounds, in the results of this paper too, the selection method matters. The CDK is used to calculate fingerprints.

Smusz, S., Kurczab, R., Bojarski, A. J., Apr. 2013. The influence of the inactives subset generation on the performance of machine learning methods. Journal of Cheminformatics 5 (1), 17+. URL

Using fingerprints to create clustering trees

I need to read this paper by Palacios-Bejarano et al. in more detail, because it seems quite interesting. The use fingerprints to make clustering trees, which, if I understand it correctly, be used to calculate similarities between molecules. That is used in QSAR modeling of the CLogP, and the results suggest that while MCS works better, this approach is more robust. This paper too uses the CDK for fingerprint calculation.

Palacios-Bejarano, B., Cerruela Garcia, G., Luque Ruiz, I., Gómez-Nieto, M., Jun. 2013. An algorithm for pattern extraction in fingerprints. Chemometrics and Intelligent Laboratory Systems 125, 87-100. URL

Dr. J. Alvarsson: Bioclipse 2, signature fingerprints, and chemometrics

Last Friday I attended the PhD defense of, now, Dr. Jonathan Alvarsson (Dept. Pharmaceutical Biosciences, Uppsala University), who defended his thesis Ligand-based Methods for Data Management and Modelling (PDF). Key papers resulting from his work include (see the list below) one about Bioclipse 2, particularly covering his work on plug-able managers that enrich scripting languages (JavaScript, Python, Groovy) with domain specific functionality, which I make frequent use of (doi:10.1186/1471-2105-10-397), a paper about Brunn, a LIMS system for microplates, which is based on Bioclipse 2 (doi:10.1186/1471-2105-12-179), and a set of chemometrics papers, looking at scaling up pattern recognition via QSAR model buildings (e.g. doi:10.1021/ci500361u). He is also author on several other papers and we collaborated on several of them, so you will find his name in several more papers. Check his Google Scholar profile.

In Sweden there is one key opponent, though further questions can be asked by a few other committee members. John Overington (formerly of ChEMBL) was the opponent and he asked Jonathan questions for at least an hour, going through the thesis. Of course, I don't remember most of it, but there were a few that I remember and want to bring up. One issue was about the uptake of Bioclipse by the community, and, for example, how large the community is. The answer is that this is hard to answer; there are download statistics and there is actual use.

Download statistics of the Bioclipse 2.6.1 release.
Like citation statistics (the Bioclipse 1 paper was cited close to 100 times, Bioclipse 2 is approaching 40 citations), download statistics reflect this uptake but are hardly direct measurements. When I first learned about Bioclipse, I realized that it could be a game changer. But it did not. I still don't quite understand why not. It looks good, is very powerful, very easy to extend (which I still make a lot of use of), it is fairly easy to install (download 2.6.1 or the 2.6.2 beta), etc. And it resulted in a large set of applications, just check the list of papers.

One argument could be, it is yet another tool to install, and developers are turning to web-based solutions. Moreover, the cheminformatics community has many alternatives and users seem to prefer smaller, more dedicated tools, like a file format converter, like Open Babel, or a dedicated descriptor calculator, like PaDEL. Simpler messages seem more successful; this is expected for politics, but I guess science is more like politics that we like to believe.

A second question I remember was about what Jonathan would like to see changed in ChEMBL, the database Overington has worked so long on. As a data analyst you are in a different mind set: rather than thinking about single molecules, you think about classes of compounds, and rather than thinking about the specific interaction of a drug with a protein, you think about the general underlying chemical phenomenon. A question like this one requires a different kind of thinking: it needs one to think like an analytical chemist, that worries about the underlying experiments. Obvious, but easy to return too once thinking at a higher (different) level. That experimental error information in ChEMBL can actually support modelling, is something we showed using Bayesian statistics (well, Martin Eklund particularly) in Linking the Resource Description Framework to cheminformatics and proteochemometrics (doi:10.1186/2041-1480-2-S1-S6) by including the assay confidence assigned by the ChEMBL curation team. If John would have asked me, I would have said I wanted ChEMBL to capture as much of the experimental details as possible.

Integration of RDF technologies in Bioclipse. Alvarsson worked on the integration of the RDF editor in Bioclipse.
The screenshot shows that if you click a RDF resource reflecting a molecule, it will show the structure (if there is a
predicte providing the SMILES) and information by predicates in general.
The last question I want to discuss was about the number of rotable bonds in paracetamol. If you look at this structure, you would identify four purely σ bonds (BTW, can you have π bonds without sigma bonds?). So, four could be the expected answer. You can argue that the peptide bond should not be considered rotatable, and should be excluded, and thus the answer would be two. Now, the CDK answers two, as shown in an example of descriptor calculation in the thesis. I raised my eyebrows, and thought: "I surely hope this is not a bug!". (Well, my thoughts used some more words, which I will not repeat here.)

But thinking about that, I valued the idea of Open Source: I could just checked, and took my tablet from my bag, opened up a browser, went to GitHub, and looked up the source code. It turned out it was not a bug! Phew. No, in fact, it turned out that the default parameters of this descriptor excludes the terminal rotatable bonds:

So, problem solved. Two follow up questions, though: 1. can you look up source code during a thesis defense? Jonathan had his laptop right in front of him. I only thought of that yesterday, when I was back home, having dinner with the family. 2. I wonder if I should discuss the idea of parameterizable descriptors more; what do you think? There is a lot of confusion about this design decision in the CDK. For example, it is not uncommon that the CDK only calculates some two hundred descriptor values, whereas tool X calculates more than a thousand. Mmmm, that always makes me question the quality of that paper in general, but, oh well...

There also was a nice discussion about chemometrics. Jonathan argues in his thesis that a fast modeling method may be a better way forward at this moment, and more powerful statistical methods. He presented results with LIBLINEAR and signature fingerprints, comparing it to other approaches. The latter was compared with industry standards, like ECFP (which Clark and Ekins implemented for the CDK and have been using in Bayesian statistics on the mobile phone), and for the first Jonathan showed that LINLINEAR can handle more data than regular SVM libraries, and that the using more training data still improves the model more than a "better" statistical method (which quite matches my own experiences). And with SVMs, finding the right parameters typically is an issue. Using a RBF kernel only adds one, and since Jonathan also indicated that the Tanimoto distance measure for fingerprints is still a more than sufficient approach, which makes me wonder if the chemometrics models should not be using a Tanimoto kernel instead of a RBF kernel (though doi:10.1021/ci800441c suggests RBF may really do better for some tasks, but at the expense of more parameter optimization needed).

To wrap up, I really enjoyed working with Jonathan a lot and I think he did excellent multidisciplinary work. I am also happy that I was able to attend his defense and the events around that. In no way does this post do justice or reflect the defense; it merely reflects that how relevant his research is in my opinion, and just highlights some of my thoughts during (and after) the defense.

Jonathan, congratulations!

Spjuth, O., Alvarsson, J., Berg, A., Eklund, M., Kuhn, S., Mäsak, C., Torrance, G., Wagener, J., Willighagen, E. L., Steinbeck, C., Wikberg, J. E., Dec. 2009. Bioclipse 2: A scriptable integration platform for the life sciences. BMC Bioinformatics 10 (1), 397+.
Alvarsson, J., Andersson, C., Spjuth, O., Larsson, R., Wikberg, J. E. S., May 2011. Brunn: An open source laboratory information system for microplates with a graphical plate layout design process. BMC Bioinformatics 12 (1), 179+.
Alvarsson, J., Eklund, M., Engkvist, O., Spjuth, O., Carlsson, L., Wikberg, J. E. S., Noeske, T., Oct. 2014. Ligand-Based target prediction with signature fingerprints. J. Chem. Inf. Model. 54 (10), 2647-2653.

Monday, May 25, 2015

Bioclipse 2.6.2 with recent hacks #3: using functionality from the OWLAPI

Update: if you had problems installing this feature, please try again. Two annoying issues have been fixed now.

Third in this series is this post about the Bioclipse plugin I wrote for the OWLAPI library. This manager I wrote to learn how the OWLAPI is working. The OWLAPI feature is available from the same update site as the Linked Data Fragments, so you can just follow the steps outlined here (if you had not already).

Using the manager
The manager has various methods, for example, for loading an OWL ontology:

ontology = owlapi.load(
  "/eNanoMapper/enanomapper.owl", null

If your ontology imports other ontologies, you may need to tell the OWLAPI first where to find those, by defining mappings. For example, I could do before making the above call:

mapper = null; // initially no mapper
mapper = owlapi.addMapping(mapper,
mapper = owlapi.addMapping(mapper,
  "" + 

I can list which ontologies have been imported with:

imported = owlapi.getImportedOntologies(ontology)
for (var i = 0; i < imported.size(); i++) {

When the ontology is successfully loaded, I can list the classes and various types of properties:

classes = owlapi.getClasses(ontology)
annotProps = owlapi.getAnnotationProperties(ontology)
declaredProps = owlapi.getPropertyDeclarationAxioms(ontology)

There likely needs some further functionality that needs adding, and I love to hear about what methods you like to see added.

Tuesday, May 19, 2015

New: DOI hyperlinks in the CDK JavaDoc

Apparently I never extended the cdk.cite JavaDoc Taglet to use DOIs from the bibliographic database to create hyperlinks in the JavaDoc. But fear no more! I have submitted a simple patch today to add these to the JavaDoc, and I assume it will be part of the next CDK release from the master branch.

Of course, many papers in this bibliographic database (i.e. this cheminf.bibx file) do not have DOIs for all papers :/

Of course, you can help out here! The only thing you need is a web browser and some knowledge how to look up DOIs for papers. Just check this blog post (from Step 4 onwards) and line 260 in cheminf.bibx to see how a DOI addition to a BibTeXML entry should look like.