Interpretable molecular encodings and representations for machine learning tasks

Interpretable molecular encodings and representations for machine learning tasks

Molecular encodings and their usage in machine learning models have demonstrated significant breakthroughs in biomedical applications, particularly in the classification of peptides and proteins. To this end, we propose a new encoding method: Interpretable Carbon-based Array of Neighborhoods (iCAN)....

Full description

Bibliographic Details
Main Authors: Moritz Weckbecker, Aleksandar Anžel, Zewen Yang, Georges Hattab
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Computational and Structural Biotechnology Journal
Subjects:
Explainable
Interpretable
Molecular encoding
Representation
Machine learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2001037024001818

Internet

http://www.sciencedirect.com/science/article/pii/S2001037024001818

Similar Items