Conformational Shifts of Stacked Heteroaromatics: Vacuum vs. Water Studied by Machine Learning.

ABSTRACT: Stacking interactions play a crucial role in drug design, as we can find aromatic cores or scaffolds in almost any available small molecule drug. To predict optimal binding geometries and enhance stacking interactions, usually high-level quantum mechanical calculations are performed. These calculations have two major drawbacks: they are very time consuming, and solvation can only be considered using implicit solvation. Therefore, most calculations are performed in vacuum. However, recent studies have revealed a direct correlation between the desolvation penalty, vacuum stacking interactions and binding affinity, making predictions even more difficult. To overcome the drawbacks of quantum mechanical calculations, in this study we use neural networks to perform fast geometry optimizations an

SUBMITTER: Loeffler JR 

PROVIDER: S-EPMC8032969 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature