PubChem-10m-t5

This model is a fine-tuned version of google/t5-v1_1-base on the sagawa/pubchem-10m-canonicalized dataset. It achieves the following results on the evaluation set:

• Loss: 0.2165
• Accuracy: 0.9190

Model description

We trained t5 on SMILES from PubChem using the task of masked-language modeling (MLM). Compared to PubChem-10m-t5, PubChem-10m-t5-v2 uses a character-level tokenizer, and it was also trained on PubChem.

Intended uses & limitations

This model can be used for the prediction of molecules' properties, reactions, or interactions with proteins by changing the way of finetuning.

Training and evaluation data

We downloaded PubChem data and canonicalized them using RDKit. Then, we dropped duplicates. The total number of data is 9999960, and they were randomly split into train:validation=10:1.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-03
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10.0

Training results