From 267c2d3c1ef11f9933f7de1a1ec2b5039b7de4bc Mon Sep 17 00:00:00 2001 From: Jianmin Wang Date: Thu, 30 Mar 2023 14:56:53 +0900 Subject: [PATCH] Update README.md --- README.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index b22f52a..4a3f66c 100644 --- a/README.md +++ b/README.md @@ -42,6 +42,7 @@ iPPIGAN_generate.ipynb ``` ## Model Metrics +### MOSES 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 @@ -51,10 +52,10 @@ 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 +* 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. +* 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. ## License Code is released under GNU AFFERO GENERAL PUBLIC LICENSE. @@ -62,5 +63,5 @@ Code is released under GNU AFFERO GENERAL PUBLIC LICENSE. ## Cite: -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 +* 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