Organic Chemistry

Driving Aspirational Process Mass Intensity Using SMART-PMI and Innovative Chemistry



An important metric for gauging the impact a synthetic route has on chemical resources, cost, and sustainability is process mass intensity (PMI). Calculating the overall PMI or step PMI for a given synthesis from a process description is more and more common across the industry. Our company has established a strong track record of delivering on our Corporate Sustainability goals, being recognized with seven EPA Green Chemistry Challenge Awards in the last 15 years. While green chemistry principles help in optimizing PMI and developing more sustainable processes, a key challenge for the field is defining what ‘good’ looks like for any given molecule. Predicting aspirational PMI for a synthetic target is not yet possible from chemical structure alone. The only tool chemists have at their disposal to predict PMI requires the synthetic route to be available, which is inherently retrospective. We have developed SMART-PMI (in-Silico MSD Aspiration-al Research Tool) to fill this glaring gap. Using only a 2D chemical structure, which enables a measure of molecular complexity, we can generate a predicted SMART-PMI using historical PMI data from our company’s clinical and commercial portfolio of processes. From this SMART-PMI prediction, we have established target ranges for Successful, World Class, and Aspirational PMI. Using this model, chemists can develop powerful synthetic strategies that make the biggest impact on PMI and, in turn, drive improvements to the model. The potential of SMART-PMI to set industry-wide aspirational PMI targets is discussed.


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Supplementary material

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Merck PMI paper supporting information
Supporting Information. Table of PMI values. Table of descriptors. Structures of compounds used to build the model. This material is available free of charge via the Internet at The SMART-PMI model can be accessed at

Supplementary weblinks

Merck PMI model
Compound Complexity Calculator USAGE: bin/ [ -sdf <sdfile> | -smi <smilefile> ] NOTES: This is an implementation of Compound Complexity for use in the SMART-PMI as described by Sherer et al. It contains derived training data as required by the described Random Forest Model in order to replicate data presented in paper as well as applying to novel data.