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The Delaney dataset is a collection of 2874 aqueous solubility measurements from this paper:

Delaney, John S. "ESOL: estimating aqueous solubility directly from molecular structure." Journal of chemical information and computer sciences 44.3 (2004): 1000-1005.

This dataset is commonly used since it's a small molecular regression dataset that's convenient for benchmarking various techniques. In this example, we train a series of different DeepChem models against this task:

  • DAGModel: In delaney_DAG.py. This model will train and converge very slowly.
  • TextCNNModel: In delaney_textcnn.py. This model featurizes compounds as SMILES strings directly and trains a convolutional network directly on the text.
  • WeaveModel: In delaney_weave.py. This model trains a weave style convolution on Delaney.
  • ChemCeption: In delaney_chemception.py. This model trains a variant of an Inception convolutional network on images generated from molecules.
  • MPNNModel: In delaney_MPNN.py. This model trains a little slower, but is faster than DAGModel.