The problem
Virtual screening (VS) is not a definitive answer to finding the
needle in the haystack but it can provide a sensible set of starting
points relative to “random”. The
available screening techniques are many and varied in design and
approach and use different molecular representations, alignments (or
not), scoring functions and if available, use information obtained from
the target protein. The merits of 2D vs 3D screening are much debated
within the literature coming down marginally favourably on the side of
2D (interestingly of course, since 2D does not inherently consider
chirality) methods being more effective at isolating true positives 1.
For 3D approaches, one must generate 1 or n conformations for input
into the VS protocol – the nature of the set of conformations (n) is
debated to some extent as one form of “flexible search”, but “extrema
representatives” is not a bad approach for ensuring some set diversity
within an ensemble (http://svl.chemcomp.com/filedetails.php?lid=813&cid=40)
Many command line tools are available: from ChemAxon (Screen3D/DISCO)
or other commercial vendors (Schrodinger,OpenEye,Cresset…) are astute
choices, but if you don’t accept the Doctor’s prescription, you might
already have written your own approach in Java or C++ (or some scripting
language) and are wondering how to integrate it into IJC, so as to make
use of the data management capabilities which will assist you before
and after the VS events…
A proposed solution
IJC is exceedingly helpful for the administrator and allows for the
rapid creation of 2D databases very easily. From this point calculator
plugins can be called and used in your VS if you prefer properties
similarity. Beyond this, you can access internal functionality and
numerical data items exposed through the Chemical terms or the JCHEM API
or derived in your own indexes.
An entry point or interface between IJC and VS is the built-in Groovy
language and we have provided two scripts in the script repository:
http://www.chemaxon.com/instantjchem/ijc_latest/docs/developer/scripts/CallExternalTool.html
This can assist you in getting going, deploying the VS tool of your
choice, closely integrated with IJC Structure based entities for ease of
management & analysis, before and after…What do we have to do, we hear you cry?
I. Obviously, use your target Structural data (SDF) set to screen and
load it into IJC via standard import – this will generate a CD_ID
primary key handle per molecule. Perhaps, you will use your own in-house
data or some benchmark sets (for example DUD or WOMBAT).
II. Obtain the script (amend it, is likely required) and plugin your
choice of VS tool i.e. change the command line call example 1 (don’t
forget your query – which you will need!) [http://www.sciencedaily.com/releases/2001/11/011119072232.htm]
III. For all those whom prefer their own bespoke approaches – it’s
still OK – just write your groovy code directly at the data tree level
and access the API (and import any other java you like, for example the
JAMA libraries are highly useful for more complex matrix operations: (http://math.nist.gov/javanumerics/jama/).
For example you can extend script example 2 if it is a 3D
approach with your choice of 3D coordinates generation & force
field.
IV. The results will be built back into the entity with this
scripting – for further your further analysis of the rank orders
derived.
We would be interested to hear about your integration experiences…
Can you successfully integrate your choice of tool (s) into IJC with the above general approach?
Which approaches do you observe are most effective at predicting
viable hit options – 2D or 3D methods? Which 2D or 3D approach/paradigm
do you rate the highest or is the most reliable? Perhaps your workflow
uses both 2D and 3D concepts, in which case we would be very interested
to hear about those observations! We think reference 2 is a good option
to pursue.
References
- Brown R.D., Martin, Y. C. (1996) “Use of structure activity data to compare structure-based clustering methods and descriptors for use in compound selection”. Journal of Chemical Information and Computer Sciences 36:572-584. DOI: 10.1021/ci9501047.
- Bonachera F., Parent, B., Barbosa, F., Froloff, N., Horvath, D. (2006) “Fuzzy tricentricpharmacophore fingerprints. 1. Topological fuzzy pharmacophore triplets and adapted molecular similarity scoring schemes”. Journal of Chemical Information and Modeling 46:2457-2477. DOI: 10.1021/ci6002416.
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