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Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back
A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative s...
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| Published in: | Science (American Association for the Advancement of Science) 2023-12, Vol.382 (6677), p.eadi1407-eadi1407 |
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| cited_by | cdi_FETCH-LOGICAL-c325t-dd20164b074e34d8a1007267b86d441b3712103c0cb9535115f5cf42336d9c2c3 |
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| cites | cdi_FETCH-LOGICAL-c325t-dd20164b074e34d8a1007267b86d441b3712103c0cb9535115f5cf42336d9c2c3 |
| container_end_page | eadi1407 |
| container_issue | 6677 |
| container_start_page | eadi1407 |
| container_title | Science (American Association for the Advancement of Science) |
| container_volume | 382 |
| creator | Koscher, Brent A Canty, Richard B McDonald, Matthew A Greenman, Kevin P McGill, Charles J Bilodeau, Camille L Jin, Wengong Wu, Haoyang Vermeire, Florence H Jin, Brooke Hart, Travis Kulesza, Timothy Li, Shih-Cheng Jaakkola, Tommi S Barzilay, Regina Gómez-Bombarelli, Rafael Green, William H Jensen, Klavs F |
| description | A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In the first study, the platform experimentally realized 294 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure-function space of four rarely reported scaffolds. In each iteration, the property prediction models that guided exploration learned the structure-property space of diverse scaffold derivatives, which were realized with multistep syntheses and a variety of reactions. The second study exploited property models trained on the explored chemical space and previously reported molecules to discover nine top-performing molecules within a lightly explored structure-property space. |
| doi_str_mv | 10.1126/science.adi1407 |
| format | article |
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We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In the first study, the platform experimentally realized 294 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure-function space of four rarely reported scaffolds. In each iteration, the property prediction models that guided exploration learned the structure-property space of diverse scaffold derivatives, which were realized with multistep syntheses and a variety of reactions. 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| subjects | Absorption Absorption spectra Algorithms Artificial Intelligence Automation Batch reactors Case Studies Chemical synthesis Chemistry Closed loops Dyes Errors Experimentation Exploitation High performance liquid chromatography Iterative methods Learning algorithms Light sources Liquid chromatography Literary Devices Machine learning Molecular properties Octanol Octanol-water partition coefficients Optimization Organic Chemistry Platforms Prediction models Reconfiguration Robot arms Robot learning Robotics Scaffolds Stability Workflow |
| title | Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back |
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