How to use MOSES train/test/testSF dataset in Torchdrug

TorchDrug implements MOSES dataset, but doesn’t distinguish between train / test / testSF which MOSES has. To train GCPN on Moses, I think the correct order is to pretrain the model by train dataset at first, then train it on test / testSF dataset and finally generate the molecules. But how to do this in TorchDrug? There’s only one dataset named MOSES.

I have this question because when I generate molecules by MOSES, the statistics doesn’t look correct if compared to other models on MOSEC, especially the Scaf/Test property in the table, which tries to find out if there are same scaffolds in test dataset and generated molecules. It’s 0 for GCPN model after training on TorchDrug, following the tutorial. I think the problem is that TorchDrug only uses the train dataset but not test dataset. How can I explicitly use it? Thanks in advance!

[KiddoZhu]

Hi! There is a predefined split for MOSES implemented in TorchDrug. I am not sure if this is what you want. You can get it by

dataset = datasets.MOSES("/path/to/dataset")
train_set, valid_set, test_set = dataset.split()

Sorry I am not an expert in molecule generation. Maybe @shichence knows more about the dataset and evaluation setting on MOSES?

[KiddoZhu]

1. I think you can just create another solver wrapping the original model with test_set, load the checkpoint and finetune the model on test_set.

Pretrain:

solver = core.Engine(task, train_set, None, None, optimizer, ...)
solver.train(num_epoch=10)
solver.save("gcpn_10epoch.pkl")

Finetune:

solver = core.Engine(task, test_set, None, None, optimizer, ...)
solver.load("gcpn_10epoch.pkl")
solver.train(num_epoch=1)

The same procedure can be applied to resume training.

1. That’s great! I will follow your code and check the dataset.