Home About us MoEF Contact us Sitemap Tamil Website  
About Envis
Whats New
Research on Microbes
Microbiology Experts
Online Submission
Access Statistics

Site Visitors

blog tracking

Trends in Pharmacological Sciences
Volume 42 (3), 2021, Pages 151-165

Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation

Harini Narayanan1, Fabian Dingfelder1,2, Alessandro Butté3, Nikolai Lorenzen2, Michael Sokolov3, Paolo Arosio1

Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, Swiss Federal Institute of Technology, Zurich 8093, Switzerland.


Successful biologics must satisfy multiple properties including activity and particular physicochemical features that are globally defined as developability. These multiple properties must be simultaneously optimized in a very broad design space of protein sequences and buffer compositions. In this context, artificial intelligence (AI), and especially machine learning (ML), have great potential to accelerate and improve the optimization of protein properties, increasing their activity and safety as well as decreasing their development time and manufacturing costs. We highlight the emerging applications of ML in biologics discovery and development, focusing on protein engineering, early biophysical screening, and formulation. We discuss the power of ML in extracting information from complex datasets and in reducing the necessary experimental effort to simultaneously achieve multiple quality targets. We finally anticipate possible future interventions of AI in several steps of the biological landscape.

Keywords: machine learning, biologics development, antibodies, protein engineering, developability, formulation.

Copyright © 2005 ENVIS Centre ! All rights reserved
This site is optimized for 1024 x 768 screen resolution