QBMG: Quasi-Biogenic Molecule Generator with Deep Recurrent Network
Introduction
This is a PyTorch implementation of the paper: QBMG: quasi-biogenic molecule generator with deep recurrent neural network
The user can (i) obtain a certain number of unbiased biogenic-like molecules and (ii) obtain a certain number of focused chemotype derivatives with transfer learning.We thank the previous work by the Olivecrona team.The code in this repository is inspired on REINVENT.
Usage
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sample.py: script that is used to generate a certain number of molecules (both unbiased or certain chemotype biogenic-like molecules) with trained model.
Example:sampling 1000 molecules from unbiased model and focused chemotype model respectively
%run sample.py ./data/biogenic.ckpt 1000
%run sample.py ./data/coumarin.ckpt 1000
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transfer_learning.py: script that is used to train focused chemotype biogenic moleucles and obtain focused chemotype derivatives. The user can provide own focused chemotype molecules to generate new derivatives.
Example:training a focused chemotype library
%run transfer_learning.py ./data/coumarin.smi
Requirments
This package requires:
- Python 3.6
- PyTorch 0.4.0
- RDKit
- tqdm
- jupyter notebook
Contact
Welcome to contact us. http://www.rcdd.org.cn/home/
Use QBMG to generate fragment-like molecules
ZINC Fragment database was used to train prior model so that it can be used to generate fragment-like molecules.
Welcome to contact me: gkxiao(at)molcalx.com, replace the (at) with @.
More detailed information in Chinese:
http://blog.molcalx.com.cn/2019/02/24/deep-learning-fragment-generator.html