Engineering molecular interactions with machine learning

Receptor-binding domain-binder designs displayed on yeast. From De novo design of protein interactions with learned surface fingerprints. Reproduced under a CC BY 4.0 licence. In 2019, scientists in the joint School of Engineering and School of Life Sciences Laboratory of Protein Design and Immunoengineering (LPDI) led by Bruno Correia developed MaSIF: a machine learning-driven method for scanning millions of protein surfaces within minutes to analyze their structure and functional properties. The researchers' ultimate goal was to computationally design protein interactions by finding optimal matches between molecules based on their surface chemical and geometric "fingerprints".