A machine learning strategy for the identification of key in silico descriptors and prediction models for IgG monoclonal antibody developability properties

A machine learning strategy for the identification of key in silico descriptors and prediction models for IgG monoclonal antibody developability properties

ABSTRACTIdentification of favorable biophysical properties for protein therapeutics as part of developability assessment is a crucial part of the preclinical development process. Successful prediction of such properties and bioassay results from calculated in silico features has potential to reduce...

Full description

Bibliographic Details
Main Authors: Andrew B. Waight, David Prihoda, Rojan Shrestha, Kevin Metcalf, Marc Bailly, Marco Ancona, Talal Widatalla, Zachary Rollins, Alan C Cheng, Danny A. Bitton, Laurence Fayadat-Dilman
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:mAbs
Subjects:
Biophysical
computational
descriptors
developability
IgG1
machine learning
Online Access:https://www.tandfonline.com/doi/10.1080/19420862.2023.2248671

Internet

https://www.tandfonline.com/doi/10.1080/19420862.2023.2248671

Similar Items