Druggability Assessment in TRAPP Using Machine Learning Approaches

View Publication
Abstract:
No abstract specified

SEEK ID: https://publications.h-its.org/publications/1091

SEEK ID: https://publications.h-its.org/publications/1091

DOI: 10.1021/acs.jcim.9b01185

Research Groups: Molecular and Cellular Modeling

Publication type: Journal

Journal: Journal of Chemical Information and Modeling

Citation: J. Chem. Inf. Model. 60(3):1685-1699

Date Published: 23rd Mar 2020

URL: https://www.biorxiv.org/content/10.1101/2019.12.19.882340v1

Registered Mode: by DOI

Authors: Jui-Hung Yuan, Sungho Bosco Han, Stefan Richter, Rebecca C. Wade, Daria B. Kokh

Citation
Yuan, J.-H., Han, S. B., Richter, S., Wade, R. C., & Kokh, D. B. (2020). Druggability Assessment in TRAPP Using Machine Learning Approaches. In Journal of Chemical Information and Modeling (Vol. 60, Issue 3, pp. 1685–1699). American Chemical Society (ACS). https://doi.org/10.1021/acs.jcim.9b01185
Activity

Views: 5971

Created: 2nd Mar 2020 at 16:26

Last updated: 5th Mar 2024 at 21:24

help Tags

This item has not yet been tagged.

help Attributions

None

Powered by
 (v.1.14.2)
About HITS Publication SEEK | About HITS-Publication-SEEK | Funding and Programmes | Credits
Copyright © 2008 - 2023 The University of Manchester and HITS gGmbH