Enzyme engineering plays a central role in developing efficient biocatalysts for biotechnology, biomedicine, and life sciences. Apart from classical rational design and directed evolution approaches, machine learning methods have been increasingly applied to find patterns in data that help predict protein structures, improve enzyme stability, solubility, and function, predict substrate specificity, and guide rational protein design. In this Perspective, we analyze the state of the art in databases and methods used for training and validating predictors in enzyme engineering. We discuss current limitations and challenges which the community is facing and recent advancements in experimental and theoretical methods that have the potential to address those challenges.
Over the past century, innovations in synthetic methods have changed the way scientists think about designing and building molecules, enabling access to more expansive chemical space and to molecules possessing the essential biological activity needed in future investigational drugs. In order for the pharmaceutical industry to continue to produce breakthrough therapies that address global health needs, there remains a critical need for invention of synthetic transformations that can continue to drive new drug discovery. Toward this end, investment in research directed toward synthetic methods innovation, furthering the nexus of synthetic chemistry and biomolecules, and developing new technologies to accelerate methods discovery is essential. One powerful example of an emerging, transformative synthetic method is the recent discovery of photoredox catalysis, which allows one to harness the energy of visible light to accomplish synthetic transformations on drug-like molecules that were previously unachievable. Furthermore, recent breakthroughs in molecular biology, bioinformatics, and protein engineering are driving rapid identification of biocatalysts that possess desirable stability, unique activity, and exquisite selectivity needed to accelerate drug discovery.