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

Area of Science:

• Polymer Chemistry
• Computational Materials Science
• Biomaterials Engineering

Background:

• Chemical cross-linking modifies material properties, with molecularly imprinted polymers (MIPs) offering designable binding sites for diverse applications.
• Experimental characterization of the complex, probabilistic structures of cross-linked polymers is challenging.
• Computational prediction of polymer cross-linking is resource-intensive and underexplored.

Purpose of the Study:

• To develop a resource-efficient algorithm for predicting reactive atom pairs in pre-cross-linked polymer structures.
• To enable accurate computational modeling of molecularly imprinted nanoparticles (MINPs) and their binding characteristics.
• To provide a computational tool applicable to MIPs and other cross-linked materials.

Main Methods:

• Introduction of LNKD (Linking Nodes in KD-trees), an algorithm performing spatial queries on reactive atoms and employing a cross-linking probability function.
• Development of a protocol combining molecular dynamics simulations with LNKD for modeling MINPs.
• Validation against experimental results for MINP binding properties.

Main Results:

• LNKD accurately models MINPs and their binding properties, reproducing experimental trends.
• The algorithm identified cross-linking pairs for 88-95% of reactive atoms in tested MINPs.
• LNKD achieved this prediction in approximately three seconds on a laptop, demonstrating high efficiency.

Conclusions:

• LNKD offers a computationally efficient and accurate method for predicting cross-linking in polymers.
• The developed protocol facilitates the modeling of molecularly imprinted nanoparticles and their functional properties.
• This approach advances the computational design and characterization of cross-linked materials.