Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling
In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular des...
Saved in:
Main Authors: | Eliott Park (Author), Saeed Izadi (Author) |
---|---|
Format: | Book |
Published: |
Taylor & Francis Group,
2024-12-01T00:00:00Z.
|
Subjects: | |
Online Access: | Connect to this object online. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
13C NMR spectral data and molecular descriptors to predict the antioxidant activity of flavonoids
by: Luciana Scotti, et al.
Published: (2011) -
Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors
by: Anke Wilm, et al.
Published: (2021) -
A machine learning strategy for the identification of key in silico descriptors and prediction models for IgG monoclonal antibody developability properties
by: Andrew B. Waight, et al.
Published: (2023) -
An Explainable Supervised Machine Learning Model for Predicting Respiratory Toxicity of Chemicals Using Optimal Molecular Descriptors
by: Keerthana Jaganathan, et al.
Published: (2022) -
Analysis of Biologics Molecular Descriptors towards Predictive Modelling for Protein Drug Development Using Time-Gated Raman Spectroscopy
by: Jaakko Itkonen, et al.
Published: (2022)