Finally, we note that future work, blending this fragment-based method with distance geometry methods (e.g., ETKDG) used by RDKit will combine the speed of fragment-based placement with improved geometric and stereochemical accuracy. As noted above, fragment methods face challenges on less common, macrocyclic, or systems with overlapping fragments. On the other hand, distance-geometry methods face challenges in producing planar aromatic ring systems, which can be found in fragment databases. By combining both methods, we anticipate improved performance across multiple metrics.