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Dataset Associated with the Manuscript "Path Toward High-Throughput Synthesis Planning via Performance Benchmarking"

dataset
posted on 2024-08-09, 22:33 authored by Brandon Walker, Nathan Miller, Brett Yang, Dhatri V. L. Penna, Sridhar Vuyyuru, Jeyaraman Soundararajan, Ilia Vorontcov, Andrew Girvin, Amin Manna, Maya Choudhury, Hailey Fox, Mihir Bafna, Eduardo L. Lopez, Hugo Hernandez, Jennifer King, Thierry Masquelin, Alexander G. Godfrey, Samuel G. Michael, Gergely Zahoránszky-KőhalmiGergely Zahoránszky-Kőhalmi

In the frame of the chemistry automation program of the NCATS/NIH, called "ASPIRE", we developed an evidence-based computer aided synthesis planning (CASP) platform that is built on a central reaction knowledge graph. With the help of the ASPIRE Integrated Computational Platform's Application Programming Interface (AICP API) it is possible to identify synthesis routes for robotic platforms in an autonomous manner. The goal of the study associated with the enclosed dataset was to achieve a level of performance in terms of synthesis route identification that facilitates autonomous chemical synthesis in a high-throughput setting. A combination of network theoretical and data engineering approaches led to a breakthrough, which enabled us to realize the aim of the study.

Here we include the input dataset as well as output files generated during the various analyses of the study.

Funding

This work was supported in part by the Intramural Research Program of the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health.

History

Research Institution(s)

National Center for Advancing Translational Sciences (NCATS/NIH)

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Competing Interest Statement

Authors declare no competing interest.