Molecular Complexity: You Know It When You See It

Evaluating Molecular Complexity with Open-Source Machine Learning Approaches to Predict Process Mass Intensity

The application of green chemistry is critical for cultivating environmental responsibility and sustainable practices in pharmaceutical manufacturing. Process mass intensity (PMI) is a key metric that quantifies the resource efficiency of a manufacturing process, but determining what constitutes a successful PMI of a specific molecule is challenging. A recent approach correlated molecular features to a crowdsourced definition of molecular complexity to determine PMI targets. While recent machine learning tools show promise in predicting molecular complexity, a more extensive application could significantly optimize manufacturing processes. To this end, we refine and expand upon the SMART-PMI tool by Sheridan et al. to create an open-source model and application. Our solution emphasizes explainability and parsimony to facilitate a nuanced understanding of prediction and ensure informed decision-making. The resulting model uses four descriptors-the heteroatom count, stereocenter count, unique topological torsion, and connectivity index chi4n-to compute molecular complexity with a comparable 82.6% predictive accuracy and 0.349 RMSE. We develop a corresponding app that takes in structured data files (SDF) to rapidly quantify molecular complexity and provide a PMI target that can be used to drive process development activities. By integrating machine learning explainability and open-source accessibility, we provide flexible tools to advance the field of green chemistry and sustainable pharmaceutical manufacturing.

Palladium-catalysed fragmentary esterification-induced allylic alkylation of allyl carbonates and cyclic vinylogous anhydrides

An unprecedented palladium-catalysed fragmentary esterification-induced allylic alkylation (FEAA) of cyclic vinylogous anhydrides (CVAs) and allyl carbonates has been disclosed. The protocol features broad sp3-rich scaffold tolerance, rendering highly functionalized 1,6 and 1,7-dicarbonyls in up to high yields and diastereoselectivities. Three-component FEAA is also well tolerant to generate 1,6-dicarbonyls through the addition of extra O/N-nucleophiles. Furthermore, cyclic allyl carbonate-involved FEAA provides an efficient approach for the synthesis of structurally complex medium-sized rings.