# Computational-Assisted Development of Molecularly Imprinted Polymers for Synthetic Cannabinoid Recognition

## TL;DR

This paper uses computational methods to design polymers that can selectively detect synthetic cannabinoids, which are harmful drugs spreading globally.

## Contribution

The novel use of computational modeling to optimize molecularly imprinted polymers for synthetic cannabinoid recognition.

## Key findings

- TFAA and BA were found to form the most stable complexes with synthetic cannabinoids due to acidity and aromatic interactions.
- Six solvents were evaluated for solvation energy, identifying suitable candidates for polymerization.
- Computational predictions guide efficient experimental validation of MIPs for SC extraction.

## Abstract

Synthetic cannabinoids (SCs), a prominent class of new
psychoactive
substances, pose growing challenges to public health due to their
severe toxic effects and widespread global presence. In this study,
we employed computational methods to develop molecularly imprinted
polymers (MIPs) for the selective recognition of seven SCs, chosen
based on seizure reports from the Narcotics Examination Unit of the
Scientific Police of the State of São Paulo. Density functional
theory and extended tight binding for geometry, frequency, and noncovalent
model 2 (GFN2-xTB) calculations were used to optimize the molecular
geometries and predict ideal monomer–solvent combinations for
MIP synthesis. We assessed six solventsacetone, acetonitrile,
dichloromethane, chloroform, diethyl ether, and dimethyl sulfoxidebased
on their solvation energy, identifying suitable candidates for the
polymerization step. Hydrogen bonding interaction sites were mapped,
guiding the selection of functional monomers such as acrylic acid
(AA), 4-vinylbenzoic acid (BA), 2-(trifluoromethyl)­acrylic acid (TFAA),
and methacrylic acid. Our findings suggest that TFAA and BA offer
the most stable complexation with SCs, influenced by their acidity
and aromatic interactions. These computational predictions pave the
way for resource-efficient experimental validation and enhance the
development of MIPs as tools for the extraction of SCs in complex
matrices, contributing to efforts to combat the global SC epidemic.