Abstract
During the last decade, modern machine learning has found its way into synthetic chemistry. Some long-standing challenges, such as computer-aided synthesis planning (CASP), have been successfully addressed, while other issues have barely been touched. This Viewpoint poses the question of whether current trends can persist in the long term and identifies factors that may lead to an (un)productive development. Thereby, specific risks of molecular machine learning (MML) are discussed. Furthermore, possible sustainable developments are suggested, such as explainable artificial intelligence (exAI) for synthetic chemistry. This Viewpoint will illuminate chances for possible newcomers and aims to guide the community into a discussion about current as well as future trends.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
