The PMI Predictor app to enable green-by-design chemical synthesis


The development of sustainable processes for the synthesis of new clinical candidates is a priority for every pharmaceutical company. The ultimate efficiency of a molecule’s synthesis results from a combination of the sequence of steps to assemble the molecule and the efficiency of each of the steps. While multiple approaches are available to aid the development of efficient processes, far fewer methods to guide route innovation have been described. Here we present a ‘green-by-design’ approach to route selection and development, assisted by predictive analytics and historical data. To aid the selection of more efficient strategies, we created a user-friendly web application, the ‘PMI Predictor’ (accessible from, to predict the probable efficiencies of proposed synthetic routes before their evaluation in the laboratory. We expect that use of this app will bring greater awareness of sustainability during the initial phase of route design and will contribute to a reduced environmental impact of pharmaceutical production.

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Fig. 1: From aspirin to eribulin, brentuximab vedotin, and nusinersen.
Fig. 2: Reaction categories and subcategories used to create the dataset.
Fig. 3: Screenshots of the web app.
Fig. 4: Cumulative PMI for brivanib synthesis.
Fig. 5: Influence of development stages on cPMI model error.

Data availability

The summary of the manufacturing data that was used to support the PMI Predictor can be downloaded as an Excel spreadsheet. The full scale-up data are not publicly available due to internal restrictions of ACS GCIPR.

Code availability

The source code is available online at A DOI was created to snapshot the current state of the repository: Further details and instructions on using the tool are available in the Supplementary Information.


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We thank M. Hay and S. Vaidheeswaran for productive discussions and review of the manuscript.

Author information




A.B. acted as a project leader, co-developed the app and wrote the manuscript. J.A. wrote the app, developed the computational algorithm, and contributed to the PMI prediction model. J.L. co-developed the app, performed data mining and built the PMI prediction model. A.S.W. performed literature data collection. M.D.E. co-developed the concept of PMI prediction as a tool for green-by-design process development and co-developed the app. C.B. and I.M. performed project management and support through ACS GCIPR. B.R.D., L.J.D., J.R.G., F.G., S.G.K., M.E.K., M.O., J.L.P., F.R. and E.C.S. provided scale-up data from their individual companies and participated in project discussions and manuscript reviews. D.K.L. managed the initial stages of the project.

Corresponding author

Correspondence to Alina Borovika.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Includes: app description and use examples, data mining, permutation test statistics, synthetic schemes for examples in app performance evaluation and references.

PMI and Yield Stats for All Reactions

Includes PMI yield stats for all chemical reactions considered.

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Borovika, A., Albrecht, J., Li, J. et al. The PMI Predictor app to enable green-by-design chemical synthesis. Nat Sustain 2, 1034–1040 (2019).

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