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Driving Aspirational Process Mass Intensity Using Simple Structure-Based Prediction
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DOI：10.1021/acs.oprd.1c00477.png)
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Abstract
En 中文
An important metric for gauging the impact that 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 pharmaceutical industry, especially in process chemistry departments. As with other pharmaceutical companies, our company has established a strong track record of delivering on our Corporate Sustainability goals, being recognized with eight EPA Green Chemistry Challenge Awards in the last 15 years, and we show how these routes help define aspirational PMI targets. While green chemistry principles help in optimizing PMI and developing more sustainable processes, a key challenge for the field is defining what a good PMI for a molecule looks like given its structure alone. An existing tool that chemists have at their disposal to predict PMI requires the synthetic route be provided or proposed (e.g., via retrosynthetic analysis) which then enables practitioners to compare predicted PMIs between routes. We have developed SMART-PMI (in-Silico MSD Aspirational Research Tool) to complement existing tools by predicting PMI from molecular structure alone. Using only a 2D chemical structure, we can generate a predicted SMART-PMI from a measure of molecular complexity and molecular weight. We show how these predictions correlate with historical PMI data from our company's clinical and commercial portfolio of processes. From this SMART-PMI prediction, we have established target ranges which we termed Successful , World Class , and Aspirational PMI. The goal of this range is to set the floor for what is a good PMI for a given molecule and provide ambitious targets to drive innovative green chemistry. Using this model, chemists can develop synthetic strategies that make the biggest impact on PMI. As innovation in chemistry and processes leads to better and better PMIs, in turn, this data can drive ever more aggressive targets for the model. The potential of SMART-PMI, in combination with other existing PMI tools, to set industry-wide aspirational PMI targets is discussed.
Keywords:
process mass intensity
PMI
machine learning
green chemistry
SMART-PMI
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