Cambridge Open Engage home
What is Cambridge Open Engage?
How to Submit
Browse
About
News [opens in a new tab]

A Deep Learning Based Scaffold Hopping Strategy for the Design of Kinase Inhibitors

24 May 2021, Version 1
Working Paper
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Scaffold hopping is a widely used strategy for drug design towards kinase inhibitors. In current study, we proposed a fragment-based deep learning strategy for scaffold hopping towards the conserved hinge binding motif of kinase inhibitors in a large scale.

Keywords

Deep Learning
Molecular Generation
Scaffold Hopping
Kinase inhibitors
Fragment-based Drug Design

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.

Now Published

Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches [opens in a new tab]
Lizhao Hu, Yuyao Yang, Shuangjia Zheng, Jun Xu, Ting Ran, Hongming Chen journal article
Journal of Chemical Information and Modeling , Volume 61, Issue 10
Online publication date: Sep 29, 2021

Version History

May 24, 2021 Version 1

Version Notes

pre-submission version

Metrics

1,573
773
0
Views
Downloads
Citations

License

CC logo
CC
BY logo
BY
NC logo
NC
ND logo
ND
The content is available under CC BY NC ND 4.0 [opens in a new tab]

DOI

10.26434/chemrxiv.14633334.v1
D O I: 10.26434/chemrxiv.14633334.v1 [opens in a new tab]

Author’s competing interest statement

The authors declare no competing financial interest.

Share