>> Sorry, they can be validated to some extend using biochemical data!
>You are joking, right?

Perhaps a distinction has to be made between model validation and making
useful predictions from the model. Something like

Model Validation - testing model against data. In case of protein
crystallography the diffraction data plus other (e.g. stereochemical)
data. Can use cross validation where some of the data is put aside as a
validation set. 

Using model for making predictions (e.g. predicting results of
biochemical experiments). If a model consistently makes correct and
interesting predictions then it is useful. Eventually such success could
be regarded as validation (until the next prediction fails!).

I would not regard the two as completely separate but, for protein
crystallography, it seems to be useful to make this sort of distinction.

 Colin





-----Original Message-----
From: CCP4 bulletin board [mailto:[email protected]] On Behalf Of
Morten Kjeldgaard
Sent: 17 November 2009 09:56
To: [email protected]
Subject: Re: [ccp4bb] video that explains, very simply, what Structural
Molecular Biology is about

On 17/11/2009, at 08.10, mesters wrote:

>> Yes, but models that can be validated against experimental data.  
>> The defining characteristics of computational models is that they
>> (A) are 100% dependent on the algortihm, (B) can't be validated at 
>> all.
>>
>> Cheers,
>> Morten
> Sorry, they can be validated to some extend using biochemical data!

You are joking, right?

-- Morten

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