So much to do, so little time

Trying to squeeze sense out of chemical data

Search Result for rest — 119 articles

Substructure Matching, REST style

without comments

I’ve been putting up a number of REST services for a variety of cheminformatics tasks. One that was missing was substructure searching. In many scenarios it’s useful to be able to check whether a target molecule contains a query substructure or not. This can now be done by visiting URL’s of the form

1
http://rguha.ath.cx/~rguha/cicc/rest/substruct/TARGET/QUERY

where TARGET and QUERY are SMILES and SMARTS (or SMILES) respectively (appropriately escaped). If the query pattern is found in the target molecule then the resultant page contains the string “true” otherwise it contains the string “false”. The service uses OpenBabel to perform the SMARTS matching.

Using this service, I updated the ONS data query page to allow one to filter results by SMARTS patterns. This generally only makes sense when no specific solute is selected. However, filtering all the entries in the spreadsheet (i.e., any solvent, any solute) can be slow, since each molecule is matched against the SMARTS pattern using a separate HTTP requests. This could be easily fixed using POST, but it’s a hack anyway since this type of thing should probably be done in the database (i.e., Google Spreadsheet).

Update

The substructure search service is now updated to accept POST requests. As a result, it is possible to send in multiple SMILES strings and match them against a pattern all at one go. See the repository for a description on how to use the POST method. (The GET method is still supported but you can only match a pattern against one target SMILES). As a result, querying the ONS data using SMARTS pattens is significantly faster.

Written by Rajarshi Guha

February 3rd, 2009 at 6:01 pm

Update to the REST Descriptor Services

with 2 comments

The current version of the REST interface to the CDK descriptors allowed one to access descriptor values for a SMILES string by simply appending it to an URL, resulting in something like

http://rguha.ath.cx/~rguha/cicc/rest/desc/descriptors/
org.openscience.cdk.qsar.descriptors.molecular.ALOGPDescriptor/c1ccccc1COCC

This type of URL is pretty handy to construct by hand. However, as Pat Walters pointed out in the comments to that post, SMILES containing ‘#’ will cause problems since that character is a URL fragment identifier. Furthermore, the presence of a ‘/’ in a SMILES string necessitates some processing in the service to recognize it as part of the SMILES, rather than a URL path separator. While the service could handle these (at the expense of messy code) it turned out that there were subtle bugs.

Based on Pats’ suggestion I converted the service to use base64 encoded SMILES, which let me simplify the code and remove the bugs. As a result, one cannot append the SMILES directly to the URL’s. Instead the above URL would be rewritten in the form

http://rguha.ath.cx/~rguha/cicc/rest/desc/descriptors/
org.openscience.cdk.qsar.descriptors.molecular.ALOGPDescriptor/YzFjY2NjYzFDT0ND

All the example URL’s described in my previous post that involve SMILES strings, should be rewritten using base64 encoded SMILES. So to get a document listing all descriptors for “c1ccccc1COCC” one would write

http://rguha.ath.cx/~rguha/cicc/rest/desc/descriptors/YzFjY2NjYzFDT0ND

and then follow the links therein.

While this makes it a little harder to directly write out these URL’s by hand, I expect that most uses of this service would be programmatic – in which case getting base64 encoded SMILES is trivial.

Written by Rajarshi Guha

January 11th, 2009 at 5:52 pm

Playing with REST Descriptor Services

with 4 comments

As part of my work at IU I have been implementing a number of cheminformatics web services. Initially these were SOAP, but I realized that REST interfaces make life much easier. (also see here) As a result, a number of these services have simple REST interfaces. One such service provides molecular descriptor calculations, using the CDK as the backend. Thus by visitingĀ  (i.e., making a HTTP GET request) a URL of the form

http://rguha.ath.cx/~rguha/cicc/rest/desc/descriptors/CC(=O)

you get a simple XML document containing a list of URL’s. Each URL represents a specific “resource”. In this context, the resource is the descriptor values for the given molecule. Thus by visiting

http://rguha.ath.cx/~rguha/cicc/rest/desc/descriptors/
org.openscience.cdk.qsar.descriptors.molecular.ALOGPDescriptor/CC(=O)C

one gets another simple XML document that lists the names and values of the AlogP descriptor. In this case, the CDK implementation evaluates AlogP, AlogP2 and molar refractivity – so there are actually three descriptor values. On the other hand something like theĀ  molecular weight descriptor gives a single value. To just see the list of available descriptors visit

http://www.chembiogrid.org/cheminfo/rest/desc/descriptors

which gives an XML document containing a series of links. Visiting one of these links gives the “descriptor specification” – information on the vendor, version, reference to a descriptor ontology and so on.

(I should point out that the descriptors available in this service are from a pretty old version of the CDK. I really should update the descriptors to the 1.2.x versions)

Applications

This type of interface makes it easy to whip up various applications. One example is the PCA analysis of compound collections. Another one I put together today based on a conversation with Jean-Claude was a simple application to plot pairs of descriptor values for a collection of SMILES.

dppss1

The app is pretty simple (and quite slow, since it uses synchronous GET’s to the descriptor service for each SMILES and has to make two calls for each SMILES – hey, it was a quick hack!). Currently, it’s a bit restrictive – if a descriptor calculates multiple values, it will only use the first value. To see how many values a molecular descriptor calculates, see the list here.

With a little more effort one could easily have a pretty nice online descriptor calculation application rivaling a standalone application such as the the CDK descriptor GUI

Also,if you struggle with nice CSS layouts, the CSS Layout Collection is a fantastic resource. And jQuery rocks.

Written by Rajarshi Guha

January 7th, 2009 at 7:06 am

Extending the REST PCA Service

with 12 comments

I recently described a REST based service for performing PCA-based visualization of chemical spaces. By visiting a URL of the form

http://rguha.ath.cx/~rguha/cicc/rest/chemspace/default/
c1ccccc1,c1ccccc1CC,c1ccccc1CCC,C(=O)C(=O),CC(=O)O

one would get a HTML, plain text or JSON page containing the first two principal components for the molecules specified. With this data one can generate a simple 2D plot of the distributions of molecules in the “default” chemical space.

However, as Andrew Lang pointed out on FriendFeed, one could use SecondLife to look at 3D versions of the PCA results. So I updatesd the service to allow one to specify the number of components in the URL. The above form of the service will still work – you get the first two components by default.

To specify more components use an URL of the form

http://rguha.ath.cx/~rguha/cicc/rest/chemspace/default/3/mol1,mol2,mol3

where mol1, mol2, mol3 etc should be valid SMILES strings. The above URL will return the first three PC’s. To get just the first PC, replace the 3 with 1 and so on. If more components are requested than available, all components are returned.

Currently, the only available space is the “default” space which is 4-dimensional, so you can get a maximum of four components. In general, visit the URL

http://rguha.ath.cx/~rguha/cicc/rest/chemspace/

to obtain a list of currently available chemical spaces, their names and dimensionality.

Caveat

While it’s easy to get all the components and visualize them, it doesn’t always make sense to do so. In general, one should consider those initial principal components that explain a significant portion of the variance (see Kaisers criterion). The service currently doesn’t provide the eigenvalues, so it’s not really possible to decide whether to go to 3, 4 or more components. For most cases, just looking at the first two principal components will sufficient – especially given the currently available chemical space.

Update (Jan 13, 2009)

Since the descriptor service now requires that Base64 encoded SMILES, the example usage URL is now invalid. Instead, the SMILES should be replaced by their encoded versions. In other words the first URL above becomes

http://rguha.ath.cx/~rguha/cicc/rest/chemspace/default/
YzFjY2NjYzE=,YzFjY2NjYzFDQw==,YzFjY2NjYzFDQ0M=,
Qyg9TylDKD1PKQ==,Q0MoPU8pTw==

Written by Rajarshi Guha

January 3rd, 2009 at 1:14 am

Hit Selection When You’re Strapped for Cash

with one comment

I came across a paper from Chaput et al that describes an approach to hit selection from a virtual screen (using docking), when follow-up resources are limited (a common scenario in many academic labs). Their approach is based on using multiple docking programs. As they (and others) have pointed out, there is a wide divergence between the rankings of compounds generated using different programs. Hence the motivation for a consensus approach, based on the estimating the standard deviation (SD) of scores generated by a given program and computing the intersection of compounds whose scores are greater than 2 standard deviations from the mean, in each program. Based on this rule, they selected relatively few compounds – just 14 to 22, depending on the target and confirmed at least one of them for each target. This represents less than 0.5% of their screening deck.

However, their method is parametric – you need to select a SD threshold. I was interested in seeing whether a non-parametric, ranking based approach would allow one to retrieve a subset that included the actives identified by the authors. The method is essentially the rank product method applied to the docking scores. That is, the compounds are ranked based on their docking scores and the “ensemble rank” for a compound is the product of its ranks according to each of the four programs. In contrast to the original definition, I used a sum log rank to avoid overflow issues. So the ensemble rank for the \(i\)’th compound is given by

\(R_i = \sum_{j=1}^{4} \log r_{ij}\)

where \(r_{ij}\) is the rank of the \(i\)’th compound in the \(j\)’th docking program. Compounds are then selected based on their ensemble rank. Obviously this doesn’t give you a selection per se. Instead, this allows you to select as many compounds as you want or need. Importantly, it allows you to introduce external factors (cost, synthetic feasibility, ADME properties, etc.) as additional rankings that can be included in the ensemble rank.

Using the docking scores for Calcineurin and Histone Binding Protein (Hbp) provided by Liliane Mouawad (though all the data really should’ve been included in the paper) I applied this method using the code below

1
2
3
4
5
6
7
8
9
10
library(stringr)
d <- read.table('http://cmib.curie.fr/sites/u759/files/document/score_vs_cn.txt',
                header=TRUE, comment='')
names(d) <- c('molid', 'Surflex', 'Glide', 'Flexx', 'GOLD')
d$GOLD <- -1*d$GOLD ## Since higher scores are better
ranks <- apply(d[,-1], 2, rank)
lranks <- rowSums(log(ranks))
tmp <- data.frame(molid=d[,1], ranks, lrp=rp)
tmp <- tmp[order(tmp$lrp),]
which(str_detect(tmp$molid, 'ACTIVE'))

and identified the single active for Hbp at ensemble rank 8 and the three actives for Calcineurin at ranks 3, 5 and 25. Of course, if you were selecting only the top 3 you would’ve missed the Calcineurin hit and only have gotten 1/3 of the HBP hits. However, as the authors nicely showed, manual inspection of the binding poses is crucial to making an informed selection. The ranking is just a starting point.

Update: Docking scores for Calcineurin and Hbp are now available

Written by Rajarshi Guha

February 5th, 2016 at 1:36 am