Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory
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- Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory
- Klucznik, Tomasz; Mikulak-Klucznik, Barbara; McCormack, Michael P.; Lima, Heather; Szymkuć, Sara; Bhowmick, Manishabrata; Molga, Karol; Zhou, Yubai; Rickershauser, Lindsey; Gajewska, Ewa P.; Toutchkine, Alexei; Dittwald, Piotr; Startek, Michal P.; Kirkovits, Gregory J.; Roszak, Rafai; Adamski, Alexei; Sieredzińska, Bianka; Mrksich, Milan; Trice, Sarah L.J.; Grzybowski, Bartosz A.
- computer-assisted synthesis; organic synthesis; chemical networks and graphs; large-scale calculations; artificial intelligence; Chematica
- Issue Date
- CELL PRESS
- CHEM, v.4, no.3, pp.522 - 532
- The Chematica program was used to autonomously design synthetic pathways to eight structurally diverse targets, including seven commercially valuable bioactive substances and one natural product. All of these computer-planned routes were successfully executed in the laboratory and offer significant yield improvements and cost savings over previous approaches, provide alternatives to patented routes, or produce targets that were not synthesized previously. Although computers have demonstrated the ability to challenge humans in various games of strategy, their use in the automated planning of organic syntheses remains unprecedented. As a result of the impact that such a tool could have on the synthetic community, the past half century has seen numerous attempts to create in silico chemical intelligence. However, there has not been a successful demonstration of a synthetic route designed by machine and then executed in the laboratory. Here, we describe an experiment where the software program Chematica designed syntheses leading to eight commercially valuable and/or medicinally relevant targets; in each case tested, Chematica significantly improved on previous approaches or identified efficient routes to targets for which previous synthetic attempts had failed. These results indicate that now and in the future, chemists can finally benefit from having an “in silico colleague” that constantly learns, never forgets, and will never retire. Multistep synthetic routes to eight structurally diverse and medicinally relevant targets were planned autonomously by the Chematica computer program, which combines expert chemical knowledge with network-search and artificial-intelligence algorithms. All of the proposed syntheses were successfully executed in the laboratory and offer substantial yield improvements and cost savings over previous approaches or provide the first documented route to a given target. These results provide the long-awaited validation of a computer program in practically relevant synthetic design.
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