.. image:: https://img.shields.io/pypi/v/t5chem.svg :target: https://pypi.python.org/pypi/t5chem :alt: Latest PyPI version
A Unified Deep Learning Model for Multi-task Reaction Predictions.
It is built on huggingface transformers_ -- T5 model with some modifications.
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.. _huggingface transformers: https://github.com/huggingface/transformers
We have a docker image available here <https://hub.docker.com/repository/docker/hellojocelynlu/t5chem>__, feel free to try it out!
T5Chem can be either installed via pip or from source. We recommend to install t5chem from source.
To install from source (with latest version):
.. code:: bash
$ 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.
To install via pip
.. code:: bash
$ pip install t5chem
Call from command line:
.. code:: bash
$ 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:
.. code:: bash
$ 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 <https://yzhang.hpc.nyu.edu/T5Chem/index.html>.
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 <https://yzhang.hpc.nyu.edu/T5Chem/tutorial.html>.
Call as an API (Test a trained model):
.. code:: python
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 <https://colab.research.google.com/drive/13tJlJ5loLtws6u91shbSjuPoiA1fCSae?usp=sharing>__
Use a pretrained model in python script Colab <https://colab.research.google.com/drive/1xwz7c7q1SwwD5jEQKamo9TNCN1PKH8um?usp=sharing>__
Design your own project: predict molecular weights Colab <https://colab.research.google.com/drive/1eu22gjGJDwXy59TBL8pfDmBF5_DQXBGn?usp=sharing>__
Now we have found some installation issues on rdkit version later than 2020.09.2 (See discussion here <https://stackoverflow.com/questions/65487584/how-to-import-rdkit-in-google-colab-these-days>_)
torchtext version 0.10.0 published some backward incompatible changes. T5Chem now only tested on torchtext<=0.8.1
MIT Licence.
t5chem was written by Jocelyn Lu <jl8570@nyu.edu>_.
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
.. code:: bash
@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/