AspirinCode / drug-likeness_space

Explore drug-like space with deep generative models

Drug-likeness-Space

drug-likeness

Druglikeness may be defined as a complex balance of various molecular properties and structure features which determine whether particular molecule is similar to the known drugs. These properties, mainly hydrophobicity, electronic distribution, hydrogen bonding characteristics, molecule size and flexibility and of course presence of various pharmacophoric features influence the behavior of molecule in a living organism, including bioavailability, transport properties, affinity to proteins, reactivity, toxicity, metabolic stability and many others.

https://github.com/AspirinCode/DrugAI_Drug-Likeness

QED

quantitative estimation of drug-likeness

Bickerton, G., Paolini, G., Besnard, J. et al. Quantifying the chemical beauty of drugs. Nature Chem 4, 90–98 (2012). https://doi.org/10.1038/nchem.1243

QEPPI

quantitative estimate of protein-protein interaction targeting drug-likeness

https://github.com/ohuelab/QEPPI

Acknowledgements

We thank the authors of CRT: "Generative AI Design and Exploration of Nucleoside Analogs" for releasing their code. The code in this repository is based on their source code release (https://github.com/dd1github/Generative-AI). If you find this code useful, please consider citing their work.

Requirements

Python==3.7
pytorch==1.9.0
RDKit==2020.09.10

Training

python -u  train_main.py --max_epochs 25

Generation

novel compound generation please follow notebook:

python sampling.py --block_size=141 --vocab_size 114 --gen_size=5000

Model Metrics

Molecular Sets (MOSES), a benchmarking platform to support research on machine learning for drug discovery. MOSES implements several popular molecular generation models and provides a set of metrics to evaluate the quality and diversity of generated molecules. With MOSES, MOSES aim to standardize the research on molecular generation and facilitate the sharing and comparison of new models. https://github.com/molecularsets/moses

QEPPI

quantitative estimate of protein-protein interaction targeting drug-likeness

https://github.com/ohuelab/QEPPI

• Kosugi T, Ohue M. Quantitative estimate index for early-stage screening of compounds targeting protein-protein interactions. International Journal of Molecular Sciences, 22(20): 10925, 2021. doi: 10.3390/ijms222010925 Another QEPPI publication (conference paper)

• Kosugi T, Ohue M. Quantitative estimate of protein-protein interaction targeting drug-likeness. In Proceedings of The 18th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2021), 2021. doi: 10.1109/CIBCB49929.2021.9562931 (PDF) * © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Cation

• Jianmin. Wang, Jiashun. Mao, Meng. Wang, Xiangyang. Le, Yunyun. Wang, Explore drug-like space with deep generative models, Methods. (2023). https://doi.org/10.1016/J.YMETH.2023.01.004.

• Jianmin Wang, Yanyi Chu, Jiashun Mao, Hyeon-Nae Jeon, Haiyan Jin, Amir Zeb, Yuil Jang, Kwang-Hwi Cho, Tao Song, Kyoung Tai No, De novo molecular design with deep molecular generative models for PPI inhibitors, Briefings in Bioinformatics, 2022;, bbac285, https://doi.org/10.1093/bib/bbac285