How-to: Use Built Machine Learning Model for Property Prediction/Verification of Unseen Molecules¶
OpenEye Machine Learning floe build machine learning models that predict physical properties of small molecules. In this guide, a previously built and trained fully connected neural network model will be used to predict molecular solubility. The First step is to find the ‘Physical Property Prediction for Small Molecule using Machine Learning’ Floe.
Next, click on the barchart icon to ‘Analyze Enable’ the floe.
Let’s select a previously built neural network model for property prediction. This model has to be built using the Neural Network Based Regression Model Building floe. Using the table and model analysis from the floe report, a well fitted model can be chosen (Refer to Previous How-to on model optimization). For this guide, assume that the second model is best for our need, we need to note down the Record number #, 29 in this case.
Now on the floe page, go to the ‘Show in Project Data’ and activate the Output Model.
Once active, models can be found in the analyze page. Select the required model, i.e. #29.
This selected model will be sent to the Property Predictor floe which has already activated.
Next, add in the small molecule dataset whose property needs to be predicted.
Note
Sometimes the model is sent to the wrong input (small molecule) instead of the Tensorflow model input. Make sure you have model #29 from analyze page in the Tensorflow model input and a dataset of small molecules in the first input.
Input Data
If the molecule dataset is used as validation, and already has said properties precalculated, then select the appropriate column in ‘Validation Field’ and the floe will produce R2 and other measures between the prediction and baseline.
That’s it! Run the floe.
Note
The output floeport will look very similar to this Floereport and Analyze.
Library Details of the Floe¶
Fnn built on Tensorflow Package
Molecule Explanation built on Lime
Domain of Application built on Tensorflow Probability