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

Harini Narayanan¹, Fabian Dingfelder^1,2, 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.

Abstract

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.