This example code shows how to write a custom model using Python. This example can be used to make in-house or third party models available to all StarDrop users because the model will appear within the StarDrop interface.

StarDrop supports two types of custom model – continuous and category – both of which can be built using this example by following the comments in the code. This example will work without modification and installation instructions along with a copy of the code is provided below.

Installation instructions

To install this model:

  1. Save the file and copy it onto the StarDrop IM Server (model server) onto the model files directory. By default this will be in /usr/local/StarDrop/ims/models.
  2. Restart the model server (you will need to be logged in as root to do this) using the command “/etc/init.d/StarDrop_IMServer restart”.
  3. Start the StarDrop client and, assuming it is configured to use the model server, you will see this model appear in the list of available models.

Example code

Below you can find a copy of the code in the downloadable file. Left as it is it will provide an example continuous model. however, following the comments below it can easily be changed into a categorical model instead.

from implugin import IMPlugin # this is required for all models

## Comment out the line below if this is a category model
from modeldescriptions import ContModelDescription

## Uncomment the line below if this is acategory model
# from modeldescriptions import CatModelDescription

class Plugin (IMPlugin):
def __init__(self):

## Comment out these 8 lines to make this a category model
model = ContModelDescription(‘MOD_CONT’,
0.0, # minimum possible value of prediction
1000.0, #  maximum possible value of prediction
interactive = True,
shortName = ‘name’,
longName = ‘a continuous model’,
description = ‘This is an example’)

## Uncomment these 7 lines to make this a category model
# model = CatModelDescription(‘MOD_CAT’,
#         [‘low’,’medium’,’high’], # possible result categories
#         interactive=True,
#         header=’3Cat’,
#         shortName=’3-category model’,
#         longName=’a three category model’,
#         description=’An example category model’)

IMPlugin.__init__(self, model)

# The predict function is where you need to add the custom code
# for calculation or to call out to other applications. The molecule
# is appsed to this function as a SMILES string

def predict(self, smiles, extra):
# implement model calculation here
## Comment out the lne below is this is a category model
return [7.2, 0.5] # [prediction, standard deviation]
## Uncomment the line below if this is a category model
# return [0.3,0.1,0.6] # probability of each category (listed above)
## For category models the “predicted category” is the one with the
## highest probability (in this case ‘high’)

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