Search for: All records

Small molecule solutions to many contemporary societal challenges await discovery, but the artisanal and manual process via which this class of chemical matter is typically accessed limits the discovery of new functions. Automated assembly of (N‐methyl iminodiacetic acid) MIDA or (tetramethyl N‐methyl iminodiacetic acid) TIDA boronate building blocks via iterative C─C bond formation, an approach we call “block chemistry”, alternatively enables generalized and automated preparation of many different types of small molecules in a modular fashion. But in its current form, this engine cannot also leverage nitrogen atoms as iteration handles. Here, we disclose a new iteration‐enabling group, CbzT (p‐TIDA boronate‐substituted carboxybenzyl), that reversibly attenuates the reactivity of nitrogen atoms and enables generalized catch‐and‐release purification. CbzT is leveraged to achieve the automated modular synthesis of Imatinib (Gleevec), an archetypical clinically approved kinase inhibitor, in which building blocks are iteratively linked by both N─C and C─C bonds. This work substantially expands the types of small molecules that can be iteratively assembled in an automated modular fashion. It also advances the concept of intentionally developing chemistry that machines can do. 

Abstract Many of the greatest challenges facing society today likely have molecular solutions that await discovery. However, the process of identifying and manufacturing such molecules has remained slow and highly specialist dependent. Interfacing the fields of artificial intelligence (AI) and synthetic organic chemistry has the potential to powerfully address both limitations. The Molecule Maker Lab Institute (MMLI) brings together a team of chemists, engineers, and AI‐experts from the University of Illinois Urbana‐Champaign (UIUC), Pennsylvania State University, and the Rochester Institute of Technology, with the goal of accelerating the discovery, synthesis and manufacture of complex organic molecules. Advanced AI and machine learning (ML) methods are deployed in four key thrusts: (1) AI‐enabled synthesis planning, (2) AI‐enabled catalyst development, (3) AI‐enabled molecule manufacturing, and (4) AI‐enabled molecule discovery. The MMLI's new AI‐enabled synthesis platform integrates chemical and enzymatic catalysis with literature mining and ML to predict the best way to make new molecules with desirable biological and material properties. The MMLI is transforming chemical synthesis and generating use‐inspired AI advances. Simultaneously, the MMLI is also acting as a training ground for the next generation of scientists with combined expertise in chemistry and AI. Outreach efforts aimed toward high school students and the public are being used to show how AI‐enabled tools can help to make chemical synthesis accessible to nonexperts. 

Iterating machine learning with robotic experimentation uncovered higher-yielding conditions for a common coupling reaction.