Conformational analysis of macrocyclic compounds using a machine-learned interatomic potential

Macrocyclic compounds play a vital role in many chemical and biological systems, yet their conformational analysis remains a significant challenge. In this work, we investigate the conformational landscape of macrocyclic compounds using a machine-learned interatomic potential (MLIP) based on a Nequip-like graph neural network. This MLIP is trained on the energy differences between ωB97XD3 and GFN1-xTB. The model not only reproduces the DFT relative conformer energies of the macrocycles with high fidelity but also yields optimized structures that are practically identical to those obtained via density functional theory. Furthermore, when integrated into a metadynamics-based conformational sampling framework (CREST), we recover structures that very closely match the structure obtained after gas-phase optimization with DFT starting from the crystal structure. These results underscore the potential of machine learning to overcome longstanding challenges in the conformational analysis of complex macrocyclic systems.