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Steric molecular descriptors designed for machine learning (ML) applications are critical for connecting structure-function relationships to mechanistic insight. However, many of these descriptors are not suitable for application to com-plex systems, such as catalyst reactive site pockets. In this context, we recently disclosed a new set of 3D steric molecular descriptors that were originally designed for dirhodium(II) tetra-carboxylate catalysts. Herein, we expand the Spatial Molding for Rigid Targets (SMART) descriptor toolkit by releasing SMARTpy; an automated, open-source Python API package for computational workflow integration of SMART descriptors. The impact of the structure of the molecular probe for generation of SMART descriptors was analyzed. Resultant SMART descriptors and pocket features were found to be highly dependent upon probe selection, and do not scale linearly. Flexible probes with smaller substituents can explore narrow pocket regions resulting in a higher resolution pocket imprint. Macrocyclic probes with larger substituents are more applicable to larger cavities with smooth boundaries, such as dirhodium paddlewheel complexes. In these cases, SMARTpy provides comparable descriptors to the original calculation method using UCSF Chimera. Finally, we analyzed a series of case studies demonstrating how SMART descriptors can impact other areas of catalysis, such as organocatalysis, biocatalysis, and protein pocket analysis.