1
|
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.
Collapse
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
| |
Collapse
|
2
|
Grebner C, Matter H, Hessler G. Artificial Intelligence in Compound Design. Methods Mol Biol 2021; 2390:349-382. [PMID: 34731477 DOI: 10.1007/978-1-0716-1787-8_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Artificial intelligence has seen an incredibly fast development in recent years. Many novel technologies for property prediction of drug molecules as well as for the design of novel molecules were introduced by different research groups. These artificial intelligence-based design methods can be applied for suggesting novel chemical motifs in lead generation or scaffold hopping as well as for optimization of desired property profiles during lead optimization. In lead generation, broad sampling of the chemical space for identification of novel motifs is required, while in the lead optimization phase, a detailed exploration of the chemical neighborhood of a current lead series is advantageous. These different requirements for successful design outcomes render different combinations of artificial intelligence technologies useful. Overall, we observe that a combination of different approaches with tailored scoring and evaluation schemes appears beneficial for efficient artificial intelligence-based compound design.
Collapse
Affiliation(s)
- Christoph Grebner
- Sanofi-Aventis Deutschland GmbH, R&D, Integrated Drug Discovery, Frankfurt am Main, Germany
| | - Hans Matter
- Sanofi-Aventis Deutschland GmbH, R&D, Integrated Drug Discovery, Frankfurt am Main, Germany
| | - Gerhard Hessler
- Sanofi-Aventis Deutschland GmbH, R&D, Integrated Drug Discovery, Frankfurt am Main, Germany.
| |
Collapse
|