The Open Reaction Database (ORD) is an open-access schema and infrastructure for structuring and sharing organic reaction data, including a centralized data repository. The ORD schema supports conventional and emerging technologies, from benchtop reactions to automated high-throughput experiments and flow chemistry. Our vision is that a consistent data representation and infrastructure to support data sharing will enable downstream applications that will greatly improve the state of the art with respect to computer-aided synthesis planning, reaction prediction, and other predictive chemistry tasks.

Since our initial meeting in October 2019, the database has grown to include more than 2M reactions (including a large dataset of reactions extracted from USPTO sources) and received contributions from academic and industrial users, both from published and unpublished work. Some of our current efforts include:

  • Improving user interfaces and providing support to contributors on GitHub and via email.
  • Working with companies to incorporate the ORD schema into their reaction informatics infrastructure, including the development of "translators" between the ORD schema and electronic lab notebooks (ELNs).
  • Engaging with journals and other stakeholders to drive adoption of the ORD schema as a FAIR data structure for sharing reaction data across academia, government, and industry.

Please reach out to help@open-reaction-database.org for help preparing a contribution or to discuss using the ORD in your company or lab.

Publications and Media

Journal Articles
  • Kearnes SM, Maser MR, Wleklinski M, Kast A, Doyle AG, Dreher SD, Hawkins JM, Jensen KF, Coley CW. The Open Reaction Database. J Am Chem Soc 2021, 143(45), 18820-18826. (JACS)
  • Mercado R, Kearnes SM, Coley C. Data Sharing in Chemistry: Lessons Learned and a Case for Mandating Structured Reaction Data. J Chem Inf Model 2023, 63(14), 4253-4265. (JCIM)
  • A new database for machine-learning research (C&EN, 22 November 2021)
  • Yield-predicting AI needs chemists to stop ignoring failed experiments (Chemistry World, 12 May 2022)
  • Chemists debate machine learning's future in synthesis planning and ask for open data (C&EN, 18 May 2022)
  • For chemists, the AI revolution has yet to happen (Nature, 17 May 2023)


Governing Committee

  • Connor Coley (MIT, C-CAS)
  • Abby Doyle (UCLA, C-CAS)
  • Spencer Dreher (Merck)
  • Joel Hawkins (Pfizer)
  • Klavs Jensen (MIT)
  • Steven Kearnes (Relay)

Advisory Board

  • Alán Aspuru-Guzik (Toronto, MADNESS)
  • Timothy Cernak (Michigan, Entos)
  • Lucy Colwell (Cambridge, SynTech, Google)
  • Werngard Czechtizky (AstraZeneca)
  • JW Feng
  • Matthew Gaunt (Cambridge, SynTech)
  • Alex Godfrey (NCATS Consultant)
  • Mimi Hii (Imperial, ROAR)
  • Greg Landrum (T5 Informatics)
  • Fabio Lima (Novartis)
  • Christos Nicolaou (Recursion)
  • Sarah Reisman (Caltech)
  • Francesco Rianjongdee (GSK)
  • Matthew Sigman (Utah, C-CAS)
  • Jay Stevens (BMS)
  • Sarah Trice (XtalPi)
  • Huimin Zhao (UIUC, MMLI)


We gratefully acknowledge support from:

  • Google
  • Relay Therapeutics
  • Schmidt Futures
  • University of Notre Dame