Generative Models for Automatic Chemical Design
Name
1907.01632.pdf
Description
Accepted version
Size
2.37 MB
Format
Unknown
Checksum (MD5)
1dc08777b280f1c1a46f5337c7b8de7f
Author(s)
Schwalbe-Koda, Daniel
Gómez-Bombarelli, Rafael
Date Issued
June 2020
Journal
Machine Learning Meets Quantum Physics
Publisher
Springer International Publishing
Citation
Schwalbe-Koda, D and Gómez-Bombarelli, R. 2020. "Generative Models for Automatic Chemical Design." 968.
Version
Author's final manuscript
Abstract
© 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. Materials discovery is decisive for tackling urgent challenges related to energy, the environment, health care, and many others. In chemistry, conventional methodologies for innovation usually rely on expensive and incremental strategies to optimize properties from molecular structures. On the other hand, inverse approaches map properties to structures, thus expediting the design of novel useful compounds. In this chapter, we examine the way in which current deep generative models are addressing the inverse chemical discovery paradigm. We begin by revisiting early inverse design algorithms. Then, we introduce generative models for molecular systems and categorize them according to their architecture and molecular representation. Using this classification, we review the evolution and performance of important molecular generation schemes reported in the literature. Finally, we conclude highlighting the prospects and challenges of generative models as cutting edge tools in materials discovery.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Terms of Use
Attribution-NonCommercial-ShareAlike 4.0 International
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-030-40245-7_21
