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Seifrid M, Hickman RJ, Aguilar-Granda A, Lavigne C, Vestfrid J, Wu TC, Gaudin T, Hopkins EJ, Aspuru-Guzik A. Routescore: Punching the Ticket to More Efficient Materials Development. ACS CENTRAL SCIENCE 2022; 8:122-131. [PMID: 35106378 PMCID: PMC8796309 DOI: 10.1021/acscentsci.1c01002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Indexed: 05/08/2023]
Abstract
Self-driving laboratories, in the form of automated experimentation platforms guided by machine learning algorithms, have emerged as a potential solution to the need for accelerated science. While new tools for automated analysis and characterization are being developed at a steady rate, automated synthesis remains the bottleneck in the chemical space accessible to self-driving laboratories. Combining automated and manual synthesis efforts immediately significantly expands the explorable chemical space. To effectively direct the different capabilities of automated (higher throughput and less labor) and manual synthesis (greater chemical versatility), we describe a protocol, the RouteScore, that quantifies the cost of combined synthetic routes. In this work, the RouteScore is used to determine the most efficient synthetic route to a well-known pharmaceutical (structure-oriented optimization) and to simulate a self-driving laboratory that finds the most easily synthesizable organic laser molecule with specific photophysical properties from a space of ∼3500 possible molecules (property-oriented optimization). These two examples demonstrate the power and flexibility of our approach in mixed synthetic planning and optimization and especially in downselecting promising candidates from a large chemical space via an a priori estimation of the synthetic costs.
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Affiliation(s)
- Martin Seifrid
- Department
of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Riley J. Hickman
- Department
of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Andrés Aguilar-Granda
- Department
of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Cyrille Lavigne
- Department
of Computer Science, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Jenya Vestfrid
- Department
of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Tony C. Wu
- Department
of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Théophile Gaudin
- Department
of Computer Science, University of Toronto, Toronto, Ontario M5S 3H6, Canada
- IBM
Research Zürich, 8803 Rüschlikon, Zürich, Switzerland
| | - Emily J. Hopkins
- Department
of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Alán Aspuru-Guzik
- Department
of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, Toronto, Ontario M5S 3H6, Canada
- CIFAR
Artificial Intelligence Research Chair, Vector Institute, Toronto, Ontario M5S 1M1, Canada
- Lebovic
Fellow, Canadian Institute for Advanced
Research (CIFAR), Toronto, Ontario M5S 1M1, Canada
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