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The splitter calculates the MW of each sample by converting the SMILES string from the `samples.compounds_df` in RDKit, sorts the samples by MW, and returns the sample indices needed to create the train/valid/test datasets.\r\n\r\nThis adds the ability to train on small molecules and predict on larger ones.\r\n\r\nThe current MW split class will split the indices up based on `frac_train`, `frac_valid`, and `frac_test` values passed to the `split` function.  The way that the splitting occurs, training dataset will always contain the lowest MW samples, test dataset will always contain the highest MW samples, and valid dataset will contain intermediates MW samples.\r\n\r\nIn the future, it would be good to:\r\n- Add an option to specify the split MW's directly (in dalton units)\r\n- Add an option to split the valid/test datasets randomly rather than by MW\r\n- Calculate MW statistics within each dataset (mean, 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