Opportunities for Machine Learning and Artificial Intelligence to Advance Synthetic Drug Substance Process Development
Name
griffin-et-al-2023-opportunities-for-machine-learning-and-artificial-intelligence-to-advance-synthetic-drug-substance.pdf
Description
Published version
Size
2.13 MB
Format
Adobe PDF
Checksum (MD5)
937b7936beaf9c4d68c95ba2c7d7148c
Author(s)
Griffin, Daniel J
Coley, Connor W
Frank, Scott A
Hawkins, Joel M
Jensen, Klavs F
Date Issued
November 17, 2023
Journal
Organic Process Research & Development
Publisher
American Chemical Society
Citation
Griffin, Daniel J, Coley, Connor W, Frank, Scott A, Hawkins, Joel M and Jensen, Klavs F. 2023. "Opportunities for Machine Learning and Artificial Intelligence to Advance Synthetic Drug Substance Process Development." Organic Process Research & Development, 27 (11).
Version
Final published version
Abstract
The goals of this Perspective are threefold: (1) to inform a broad audience, including machine learning (ML) and artificial intelligence (AI) academics and professionals, about synthetic drug substance process development, (2) to break down the general synthetic drug substance process development task into more tractable subtasks, and (3) to highlight areas in which machine learning and artificial intelligence might be beneficially developed and applied. Application of machine learning and artificial intelligence to chemical synthesis of medicinal compounds has long been discussed and has resulted in the development of a number of computer-aided synthesis planning tools by both academic groups and commercial enterprises. The focus of these efforts has primarily centered on the task of retrosynthetic analysis, as seen from the perspective of a medicinal chemist. This has left significant unrealized opportunities in the application of machine learning and artificial intelligence to aid the process chemist or engineer in commercial drug substance process development.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/acs.oprd.3c00229
