Hi Andrew,
If you want to build model I guess that what you want is to get
experimental logp values.
This should give you something to start with:
select ACTIVITY_ID, MOLREGNO, STANDARD_VALUE, STANDARD_TYPE from ACTIVITIES
where STANDARD_TYPE = 'LogP' and STANDARD_VALUE is not null and
data_validity_comment is null and POTENTIAL_DUPLICATE = 0;
Eloy.
2018-08-29 14:51 GMT+01:00 TJ O'Donnell <t...@acm.org>:
> Hi Andrew
> ChEMBL 24 has compound properties in the table compound_properties. I
> think the alogp
> is computed using (Crippen) atom types and the acd_logp is uses ACD labs
> methods.
> TJ
>
> On Wed, Aug 29, 2018 at 5:52 AM Andrew Dalke <da...@dalkescientific.com>
> wrote:
>
>> Hi all,
>>
>> I am starting to put together materials for the Python/RDKit training
>> course I'm giving just before the RDKit UGM next month.
>>
>> I would like to structure part of it around the SQLite release of the
>> ChEMBL data set. More specifically, I plan to include examples of machine
>> learning with scikit-learn, using RDKit descriptors and values from ChEMBL
>> 24 (and making sure to use the new schema).
>>
>> Two problems. First, I'm not a computational chemist and I don't know
>> what would constitute a good example to use. "Good" in this case means one
>> whose outlines are well-known to likely students. Second, I don't have much
>> experience with the ChEMBL data.
>>
>> My thought is to make a logP model. The easiest would be to based it on
>> atom types. For this option, can anyone suggest where I can find logP data
>> from ChEMBL?
>>
>> Another possibility is to use a pre-existing model, like the notebook
>> George Papadatos did for Ligand-based Target Prediction at
>> http://nbviewer.jupyter.org/gist/madgpap/10457778 .
>>
>> Perhaps someone here could point me to other existing resources along
>> similar lines?
>>
>> Best regards,
>>
>> Andrew
>> da...@dalkescientific.com
>>
>>
>>
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