Quantum-level machine learning calculations to predict the PES of Selegiline

Abstract

Abstract Many drug molecules contain functional groups, resulting in a torsional barrier corresponding to rotation around the bond linking the fragments. In medicinal chemistry and pharmaceutical sciences, inclusive of drug design studies, the exact calculation of the potential energy surface of these molecular torsions is extremely important and precious. Machine learning, including deep learning, is currently one of the most rapidly evolving tools in computer-aided drug discovery and molecular simulations. In this work, we used ANI-1x neural network potential as a quantum-level machine learning to predict the PESs of the Selegiline antiparkinsonian drug molecule. Also, DFT calculations at the wB97X/6-31G(d) level of theory have been used to study the structural parameters and vibrational normal modes of the Selegiline molecule. We succeeded in calculating the vibrational frequencies, electronic energy and optimization of the molecular structure of the Selegiline using the ANI-1x dataset in a very short computing cost. From this perspective, we expect the ANI-1x dataset applied in this work to be appreciably efficient and effective in computational structure-based drug design studies.