Jupyter notebooks can be a lot of fun but in the same time very useful in the initial stage of of exploring and learning before building and scaling to a bigger project. In this case I am exploring a minimal package for developling predictive ML models for small molecules.

1. Data handling A Dataset2D and a DataStructure classes take care of loading data in different formats, filtering for chemistry, calculating from a collection of fingerprints and descriptors using multiprocessing for efficiency, scaling/normalizing and providing X and y numpy arrays to be fed to the ML models.
2. Training - hyperparameter optimisation A number of popular ML algorithms are available for regression or classification. A Classifier wrapper class takes care of setting up a grid search for hyperparameters, training the models and providing predictions
3. Testing by cross validation with calculation of error estimates
4. Visualisation of models performance using different metrics and including error estimates

MIT