Loeffler Johannes R, Fernández-Quintero Monica L, Waibl Franz, Quoika Patrick K, Hofer Florian, Schauperl Michael, Liedl Klaus R
Center of Molecular Biosciences Innsbruck, Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria.
Front Chem. 2021 Mar 26;9:641610. doi: 10.3389/fchem.2021.641610. eCollection 2021.
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 and molecular dynamics simulations of heteroaromatics stacked with toluene in vacuum and in explicit solvation. We show that the resulting energies in vacuum are in good agreement with high-level quantum mechanical calculations. Furthermore, we show that using explicit solvation substantially influences the favored orientations of heteroaromatic rings thereby emphasizing the necessity to include solvation properties starting from the earliest phases of drug design.
堆积相互作用在药物设计中起着至关重要的作用，因为我们几乎可以在任何可用的小分子药物中找到芳香核或骨架。为了预测最佳结合几何结构并增强堆积相互作用，通常会进行高级量子力学计算。这些计算有两个主要缺点：它们非常耗时，并且溶剂化只能使用隐式溶剂化来考虑。因此，大多数计算都是在真空中进行的。然而，最近的研究揭示了去溶剂化惩罚、真空堆积相互作用和结合亲和力之间的直接相关性，这使得预测更加困难。为了克服量子力学计算的缺点，在本研究中，我们使用神经网络对在真空和显式溶剂化条件下与甲苯堆叠的杂芳烃进行快速几何优化和分子动力学模拟。我们表明，在真空中得到的能量与高级量子力学计算结果吻合良好。此外，我们表明使用显式溶剂化会显著影响杂芳烃环的有利取向，从而强调了从药物设计的最早阶段就纳入溶剂化性质的必要性。
