AI-driven robotic chemist for autonomous synthesis of organic molecules

• Published in: Science advances Vol. 9; no. 44; p. eadj0461
• Main Authors: Ha, Taesin, Lee, Dongseon, Kwon, Youngchun, Park, Min Sik, Lee, Sangyoon, Jang, Jaejun, Choi, Byungkwon, Jeon, Hyunjeong, Kim, Jeonghun, Choi, Hyundo, Seo, Hyung-Tae, Choi, Wonje, Hong, Wooram, Park, Young Jin, Jang, Junwon, Cho, Joonkee, Kim, Bosung, Kwon, Hyukju, Kim, Gahee, Oh, Won Seok, Kim, Jin Woo, Choi, Joonhyuk, Min, Minsik, Jeon, Aram, Jung, Yongsik, Kim, Eunji, Lee, Hyosug, Choi, Youn-Suk
• Format: Journal Article
• Language: English
• Published: American Association for the Advancement of Science 03.11.2023
• Subjects: Applied Sciences and Engineering; Physical and Materials Sciences; SciAdv r-articles
• ISSN: 2375-2548; 2375-2548
• DOI: 10.1126/sciadv.adj0461

The automation of organic compound synthesis is pivotal for expediting the development of such compounds. In addition, enhancing development efficiency can be achieved by incorporating autonomous functions alongside automation. To achieve this, we developed an autonomous synthesis robot that harnesses the power of artificial intelligence (AI) and robotic technology to establish optimal synthetic recipes. Given a target molecule, our AI initially plans synthetic pathways and defines reaction conditions. It then iteratively refines these plans using feedback from the experimental robot, gradually optimizing the recipe. The system performance was validated by successfully determining synthetic recipes for three organic compounds, yielding that conversion rates that outperform existing references. Notably, this autonomous system is designed around batch reactors, making it accessible and valuable to chemists in standard laboratory settings, thereby streamlining research endeavors. An AI-based robot capable of goal-specific dynamic optimization of molecular synthesis recipes is demonstrated.