Model Card for ReactionT5v2-yield

This is a ReactionT5 pre-trained to predict yields of reactions. You can use the demo here.

Model Sources

• Repository: https://github.com/sagawatatsuya/ReactionT5v2
• Paper: https://arxiv.org/abs/2311.06708
• Demo: https://huggingface.co./spaces/sagawa/ReactionT5_task_yield

Uses

How to Get Started with the Model

Use the code below to get started with the model.

import torch
import torch.nn as nn
from transformers import AutoTokenizer, T5ForConditionalGeneration, AutoConfig, PreTrainedModel
import logging
logging.getLogger('transformers').setLevel(logging.ERROR)

class ReactionT5Yield(PreTrainedModel):
    config_class  = AutoConfig
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self.model = T5ForConditionalGeneration.from_pretrained(self.config._name_or_path)
        self.model.resize_token_embeddings(self.config.vocab_size)
        self.fc1 = nn.Linear(self.config.hidden_size, self.config.hidden_size//2)
        self.fc2 = nn.Linear(self.config.hidden_size, self.config.hidden_size//2)
        self.fc3 = nn.Linear(self.config.hidden_size//2*2, self.config.hidden_size)
        self.fc4 = nn.Linear(self.config.hidden_size, self.config.hidden_size)
        self.fc5 = nn.Linear(self.config.hidden_size, 1)

        self._init_weights(self.fc1)
        self._init_weights(self.fc2)
        self._init_weights(self.fc3)
        self._init_weights(self.fc4)
        self._init_weights(self.fc5)

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=0.01)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=0.01)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)

    def forward(self, inputs):
        encoder_outputs = self.model.encoder(**inputs)
        encoder_hidden_states = encoder_outputs[0]
        outputs = self.model.decoder(input_ids=torch.full((inputs['input_ids'].size(0),1),
                                            self.config.decoder_start_token_id,
                                            dtype=torch.long), encoder_hidden_states=encoder_hidden_states)
        last_hidden_states = outputs[0]
        output1 = self.fc1(last_hidden_states.view(-1, self.config.hidden_size))
        output2 = self.fc2(encoder_hidden_states[:, 0, :].view(-1, self.config.hidden_size))
        output = self.fc3(torch.hstack((output1, output2)))
        output = self.fc4(output)
        output = self.fc5(output)
        return output*100


model = ReactionT5Yield.from_pretrained('sagawa/ReactionT5v2-yield')
tokenizer = AutoTokenizer.from_pretrained('sagawa/ReactionT5v2-yield')
inp = tokenizer(['REACTANT:CC(C)n1ncnc1-c1cn2c(n1)-c1cnc(O)cc1OCC2.CCN(C(C)C)C(C)C.Cl.NC(=O)[C@@H]1C[C@H](F)CN1REAGENT: PRODUCT:O=C(NNC(=O)C(F)(F)F)C(F)(F)F'], return_tensors='pt')
print(model(inp)) # tensor([[19.1666]], grad_fn=<MulBackward0>)

Training Details

Training Procedure

We used Open Reaction Database (ORD) dataset for model training. In addition, we used palladium-catalyzed Buchwald-Hartwig C-N cross-coupling reactions dataset's test split to prevent data leakage. The command used for training is the following. For more information about data preprocessing and training, please refer to the paper and GitHub repository.

python train.py \
    --train_data_path='../data/preprocessed_ord_train.csv' \
    --valid_data_path='../data/preprocessed_ord_valid.csv' \
    --test_data_path='../data/preprocessed_ord_test.csv' \
    --CN_test_data_path='../data/C_N_yield/MFF_Test1/test.csv' \
    --epochs=100 \
    --batch_size=32 \
    --output_dir='./'

Results

R^2	DFT	MFF	Yield-BERT	T5Chem	CompoundT5	ReactionT5 (without finetuning)	ReactionT5
Random 70/30	0.92	0.927 ± 0.007	0.951 ± 0.005	0.970 ± 0.003	0.971 ± 0.002	0.831 ± 0.012	0.947 ± 0.003
Test 1	0.80	0.851	0.838	0.811	0.855	0.846	0.872
Test 2	0.77	0.713	0.836	0.907	0.852	0.869	0.917
Test 3	0.64	0.635	0.738	0.789	0.712	0.779	0.811
Test 4	0.54	0.184	0.538	0.627	0.547	0.843	0.830
Avg. Tests 1–4	0.69 ± 0.104	0.596 ± 0.251	0.738 ± 0.122	0.785 ± 0.094	0.741 ± 0.126	0.834 ± 0.034	0.857 ± 0.041

Citation

arxiv link: https://arxiv.org/abs/2311.06708

@misc{sagawa2023reactiont5,  
      title={ReactionT5: a large-scale pre-trained model towards application of limited reaction data}, 
      author={Tatsuya Sagawa and Ryosuke Kojima},  
      year={2023},  
      eprint={2311.06708},  
      archivePrefix={arXiv},  
      primaryClass={physics.chem-ph}  
}