Quantum-level machine learning calculations of Levodopa.
Comput Biol Chem
; 112: 108146, 2024 Jul 14.
Article
in En
| MEDLINE
| ID: mdl-39067350
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 (PES) of these molecular torsions is extremely important and precious. Machine learning (ML), including deep learning (DL), 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 ML to predict the PESs of the L-3,4-dihydroxyphenylalanine (Levodopa) antiparkinsonian drug molecule. The electronic energies and structural parameters calculated by density functional theory (DFT) using the wB97X method and all possible Pople's basis sets indicated the 6-31G(d) basis set, when used with the wB97X functional, exhibits behavior similar to that of the ANI-1x model. The vibrational frequencies investigation showed a linear correlation between DFT and ML data. All ANI-1x calculations were completed quickly in a very short computing time. 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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Comput Biol Chem
Journal subject:
BIOLOGIA
/
INFORMATICA MEDICA
/
QUIMICA
Year:
2024
Document type:
Article