AUTHOR=Alshehri Abdulelah S. , You Fengqi TITLE=Paradigm Shift: The Promise of Deep Learning in Molecular Systems Engineering and Design JOURNAL=Frontiers in Chemical Engineering VOLUME=3 YEAR=2021 URL=https://www.frontiersin.org/journals/chemical-engineering/articles/10.3389/fceng.2021.700717 DOI=10.3389/fceng.2021.700717 ISSN=2673-2718 ABSTRACT=

The application of deep learning to a diverse array of research problems has accelerated progress across many fields, bringing conventional paradigms to a new intelligent era. Just as the roles of instrumentation in the old chemical revolutions, we reinforce the necessity for integrating deep learning in molecular systems engineering and design as a transformative catalyst towards the next chemical revolution. To meet such research needs, we summarize advances and progress across several key elements of molecular systems: molecular representation, property estimation, representation learning, and synthesis planning. We further spotlight recent advances and promising directions for several deep learning architectures, methods, and optimization platforms. Our perspective is of interest to both computational and experimental researchers as it aims to chart a path forward for cross-disciplinary collaborations on synthesizing knowledge from available chemical data and guiding experimental efforts.