期刊
ANNUAL REVIEW OF CHEMICAL AND BIOMOLECULAR ENGINEERING, VOL 12, 2021
卷 12, 期 -, 页码 15-37出版社
ANNUAL REVIEWS
DOI: 10.1146/annurev-chembioeng-101220-102232
关键词
molecular data science; chemoinformatics; machine learning; artificial intelligence; continuous representation of molecules; molecular design
资金
- NRT-DESE: Data Intensive Research Enabling Clean Technologies (DIRECT) [1633216]
- HDR: I-DIRSE-FW: Accelerating the Engineering Design and Manufacturing Life-Cycle with Data Science [1934292]
Chemical engineering is undergoing rapid transformation with the tools of data science, particularly with the growing influence of artificial intelligence applications. Molecular data science, which focuses on molecular discovery and property optimization at the atomic scale, plays a key role in this evolution.
Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between. Early adoption of data science, machine learning, and early examples of AI in chemical engineering has been rich with examples of molecular data science-the application tools for molecular discovery and property optimization at the atomic scale. We summarize key advances in this nascent subfield while introducing molecular data science for a broad chemical engineering readership. We introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations.
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