A Turing Test for Molecular Generators

Autor: Yoshiaki Washio, Alan Nadin, Mike Barker, Peter Pogány, Darren V. S. Green, Gemma V. White, Anthony W. J. Cooper, Stephen D. Pickett, Graham George Adam Inglis, Sebastien Andre Campos, Jacob T. Bush, Darren L Poole, John Pritchard, Vipulkumar Kantibhai Patel, Andrew Baxter, David Jonathan Hirst
Rok vydání: 2020
Předmět:
Zdroj: Journal of Medicinal Chemistry. 63:11964-11971
ISSN: 1520-4804
0022-2623
Popis: Machine learning approaches promise to accelerate and improve success rates in medicinal chemistry programs by more effectively leveraging available data to guide a molecular design. A key step of an automated computational design algorithm is molecule generation, where the machine is required to design high-quality, drug-like molecules within the appropriate chemical space. Many algorithms have been proposed for molecular generation; however, a challenge is how to assess the validity of the resulting molecules. Here, we report three Turing-inspired tests designed to evaluate the performance of molecular generators. Profound differences were observed between the performance of molecule generators in these tests, highlighting the importance of selection of the appropriate design algorithms for specific circumstances. One molecule generator, based on match molecular pairs, performed excellently against all tests and thus provides a valuable component for machine-driven medicinal chemistry design workflows.
Databáze: OpenAIRE
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