T5Chem

A Unified Deep Learning Model for Multi-task Reaction Predictions.

It is built on huggingface transformers -- T5 model with some modifications.

Docker

We have a docker image available here, feel free to try it out!

Installation

T5Chem can be either installed via pip or from source. We recommend to install t5chem from source.

1. To install from source (with latest version):

$ git clone https://github.com/HelloJocelynLu/t5chem.git
$ cd t5chem/
$ python setup.py install
$ python setup.py test # optional, only works when you have pytest installed

It should automatically handle dependencies for you.

2. To install via pip

$ pip install t5chem

Usage

Call from command line:

$ t5chem -h # show the general help information
$ t5chem train -h # show help information for model training
$ t5chem predict -h # show help information for model prediction

We have some sample data (a small subset from datasets used in paper) available in data/ folder, to have a quick start:

$ tar -xjvf data/sample_data.tar.bz2
$ t5chem train --data_dir data/sample/product/ --output_dir model/ --task_type product --num_epoch 30        # Train a model
$ t5chem predict --data_dir data/sample/product/ --model_dir model/      # test a trained model

These commands trained a T5Chem model from scratch and take ~13 mins in v100 GPU. It is recommended to use a prerained model rather than totally trained from scratch, you can download some trained models and more datasets here. Note that we may get a bad result (0.1% top-1 accuracy) as we are only trained on a small dataset and totally from scratch. (You will get ~70% top-1 accuracy if training from a pretrained model by using --pretrain.) A more detailed example training from pretrained weights and explanations for commonly used arguments can be find here.

Call as an API (Test a trained model):

from transformers import T5ForConditionalGeneration
from t5chem import T5ForProperty, SimpleTokenizer
pretrain_path = "path/to/your/pretrained/model/"
model = T5ForConditionalGeneration.from_pretrained(pretrain_path)    # for seq2seq tasks
tokenizer = SimpleTokenizer(vocab_file=os.path.join(pretrain_path, 'vocab.pt'))
inputs = tokenizer.encode("Product:COC(=O)c1cc(COc2ccc(-c3ccccc3OC)cc2)c(C)o1.C1CCOC1>>", return_tensors='pt')
output = model.generate(input_ids=inputs, max_length=300, early_stopping=True)
tokenizer.decode(output[0], skip_special_tokens=True) # "COc1ccccc1-c1ccc(OCc2cc(C(=O)O)oc2C)cc1"

model = T5ForProperty.from_pretrained(pretrain_path)  # for non-seq2seq task
inputs = tokenizer.encode("Classification:COC(=O)c1cccc(C(=O)OC)c1>CN(C)N.Cl.O>COC(=O)c1cccc(C(=O)O)c1", return_tensors='pt')
outputs = model(inputs)
print(outputs.logits.argmax())   # Class 3

We have Google Colab examples available! Feel free to try it out:

• Call T5Chem via CLI (command line) Colab
• Use a pretrained model in python script Colab
• Design your own project: predict molecular weights Colab

Compatibility

• Now we have found some installation issues on rdkit version later than 2020.09.2 (See discussion here)
• torchtext version 0.10.0 published some backward incompatible changes. T5Chem now only tested on torchtext<=0.8.1

Licence

MIT Licence.

Authors

t5chem was written by Jocelyn Lu.

Reference

Jieyu Lu and Yingkai Zhang., Unified Deep Learning Model for Multitask Reaction Predictions with Explanation. J. Chem. Inf. Model., 62. 1376–1387 (2022) https://pubs.acs.org/doi/abs/10.1021/acs.jcim.1c01467

@article{lu2022unified,
title={Unified Deep Learning Model for Multitask Reaction Predictions with Explanation},
author={Lu, Jieyu and Zhang, Yingkai},
journal={Journal of Chemical Information and Modeling},
year={2022},
publisher={ACS Publications}
}

Other projects in Zhang's Lab: https://www.nyu.edu/projects/yzhang/IMA/