Modeling Molecularly Imprinted Nanoparticles with LNKD: A Resource Efficient Algorithm for Polymer Cross-Linking

Chemical cross-linking has been widely used to modify the physical and chemical properties of materials. Molecularly imprinted polymers (MIPs) are a subclass of cross-linked polymers with designable binding sites that make them highly useful in a broad range of chemical and biological applications. Computational efforts to model, characterize, and design cross-linked polymers are limited in part due to challenges in obtaining their matrix-like and probabilistic structures experimentally. Computational prediction of polymer cross-linking is resource-intensive and underexplored. Here, we propose LNKD (Linking Nodes in KD-trees), a resource-efficient algorithm for predicting pairs of reactive atoms in pre-cross-linked 3D structures of monomers that applies not only to the modeling of MIPs, but also chemical cross-linking in other materials. LNKD performs a spatial query around all reactive atoms in a structure and uses a cross-linking probability function to predict pairs of atoms most likely to form cross-links. Additionally, we introduce a protocol for modeling molecularly imprinted nanoparticles (MINPs), a type of MIP, that combines molecular dynamics simulations with LNKD. We validate the method by its accurate modeling of MINPs and their binding properties in comparison to experimental results. For the MINPs tested, LNKD found cross-linking pairs for 88–95% of the 780 total reactive atoms in approximately three seconds on a laptop and docking results reproduce experimental trends in ligand binding.