Journal
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
Volume 59, Issue 51, Pages 22858-22893Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/anie.201909987
Keywords
automation; chemoinformatics; drug discovery; machine learning; materials science
Categories
Funding
- Machine Learning for Pharmaceutical Discovery and Synthesis Consortium
- DARPA Make-It program [ARO W911NF-16-2-0023]
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This two-part Review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this first part, we describe a classification for discoveries of physical matter (molecules, materials, devices), processes, and models and how they are unified as search problems. We then introduce a set of questions and considerations relevant to assessing the extent of autonomy. Finally, we describe many case studies of discoveries accelerated by or resulting from computer assistance and automation from the domains of synthetic chemistry, drug discovery, inorganic chemistry, and materials science. These illustrate how rapid advancements in hardware automation and machine learning continue to transform the nature of experimentation and modeling. Part two reflects on these case studies and identifies a set of open challenges for the field.
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