Tutorial to Generate Statistical Models¶
In this tutorial we explore how to create and train statistical models to predict molecular properties using the Pytorch library. We will use smiles to represent the molecules and use the csv file format to manipulate the molecules and their properties.
As an example, we will predict the activity coefficient_ for a subset of carboxylic acids taken from the GDB-13 database_. Firstly, We randomly takes a 1000 smiles from the database and compute the activity coefficient_ using the COSMO approach_. We store the values in the thousand.csv_ file.
A peek into the file will show you something like:
smiles,E_solv,gammas
OC(=O)C1OC(C#C)C2NC1C=C2,-11.05439751550119,8.816417146193844
OC(=O)C1C2NC3C(=O)C2CC13O,-8.98188869016993,52.806217658944995
OC(=O)C=C(C#C)C1NC1C1CN1,-11.386853547889574,6.413128231164093
OC(=O)C1=CCCCC2CC2C#C1,-10.578966144649726,1.426566948888662
Where the first column contains the index of the row, the second the solvation energy and finally the activity coefficients_ denoted as gammas. Once we have the data we can start exploring different statistical methods.
swan offers a thin interface to Pytorch. It takes yaml file as input and either train an statistical model or generates a prediction using a previously trained model. Let’s briefly explore the swan input.
Simulation input¶
A typical swan input file looks like:
dataset_file:
tests/test_files/thousand.csv
properties:
- gammas
use_cuda: True
featurizer:
fingerprint: atompair
model:
name: FingerprintFullyConnected
parameters:
input_features: 2048 # Fingerprint size
hidden_cells: 200
output_features: 1 # We are predicting a single property
torch_config:
epochs: 100
batch_size: 100
optimizer:
name: sgd
lr: 0.002
dataset_file: A csv file with the smiles and other molecular properties.
properties: the columns names of hte csv file representing the molecular properties to fit.
featurizer: The type of transformation to apply to the smiles to generates the features. Could be either fingerprint or graph.
Have a look at the Available models.
Training a model¶
In order to run the training, run the following command:
modeller --mode train -i input.yml
swan will generate a log file called output.log with a timestamp for the different steps during the training. Finally, you can see in your cwd a folder called swan_models containing the parameters of your statistical model.
It is possible to restart the training procedure by providing the --restart
option like:
modeller --mode train -i input.yml --restart
Predicting new data¶
To predict new data you need to provide some smiles for which you want to compute the properties of interest, in this case the activity coefficient_. For doing so, you need to provide in the dataset_file entry of the input.yml file the path to a csv file containing the smiles, like the smiles.csv_:
,smiles
0,OC(=O)C1CNC2C3C4CC2C1N34
1,OC(=O)C1CNC2COC1(C2)C#C
2,OC(=O)CN1CC(=C)C(C=C)C1=N
Then run the command:
modeler --mode predict -i input.yml
swan will look for a swan_model.pt file with the previously trained model and will load it.
Finally, you will find a file called “predicted.csv” with the predicted values for the activity coefficients.