Message-ID: <2064944490.183.1632427507757.JavaMail.bigchem@cpu>
Subject: Exported From Confluence
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Motivation
In OCHEM you can use a wide variety of descriptors, machine learning met=
hods and data processing techniques to build a predictive QSAR model. Howev=
er, there are several cases when you have a model that was built using some=
specific tools or custom protocol, and you would like to introduce this mo=
del to OCHEM. For this purpose you can use the "Upload stub model"=
; tool that OCHEM offers. It is a tool that allows you to create a stub OCH=
EM profile based on the real/predicted value pairs that you provide. As a r=
esult of model upload you get a public model profile similar to a normal OC=
HEM model. The difference is that it's impossible to apply this model to ne=
w compounds. Most common use cases for model upload include:
1) Publishing your datasets and model results - possibly as part of a co=
llaboration or a journal publication
2) Model analysis using some of OCHEM tools (like Nearest neighbour=
span> analysis or MMP =
analysis).
Model upload prep=
aration
Data preparation
First, you need to upload all the data records involved in the uploaded =
model. You can use the Batch dat=
a upload tool to achieve this. After your upload is done, you need to o=
rganize your data in baskets according to what your model's training and va=
lidation sets are. On creating baskets please refer to the Working with datasets section of the doc=
umentation. Once your training and validation sets are ready, you can proce=
ed to creating a model upload file.
Model upload file
A model upload file (XLS, CSV or SDF) generally contains two columns: on=
e identifies one of the OCHEM records in your training or validation sets, =
and another holds the "predicted" value for this record. Therefor=
e, the first column can be either MOLECULE or EXTE=
RNAL_ID. The second column should be named PREDICTION.
If you name the first column MOLECULE, you can fill the column with OCHE=
M - compatible molecule data. That is you can either use OCHEM molecule ids=
(e.g., M1234), smiles (e.g., CCCN), sdfs, etc. The tool =
will take this molecular data and try to find the record in your training a=
nd validation set baskets that corresponds to this molecule.
If you name the first column EXTERNAL_ID, you should fill the column wit=
h same external record identifiers that you used during your batch upload i=
n the EXTERNAL_ID column of the batch upload sheet.
You can download an example of the sample file for uploading predicted c=
lassification value for CYP inhibition using OCHEM molecule ids here - model-upload.xls
Make sure, that your uploaded file has an exact one-to-one correspondenc=
e to the contents of your training and validation set baskets. Any extra da=
ta in the model upload file, or any records in the baskets that do not have=
a corresponding predicted value in the file are considered errors.
Model upload page

On the model upload page, select your training and validation sets, sele=
ct your model upload file, and provide a brief description of the uploaded =
model.

If everything goes well and the uploaded model file does not contain any=
errors, you will be presented by an Uploaded model OCHEM profile

Otherwise, a list of errors will be presented.

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