Table_1_Conformational Shifts of Stacked Heteroaromatics: Vacuum vs. Water Studied by Machine Learning.DOCX

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.

堆叠相互作用在药物设计中发挥着至关重要的作用，因为几乎所有现有小分子药物中均存在芳香核心或骨架（scaffold）。为预测最优结合构象并强化堆叠相互作用，当前通常需开展高水平量子力学计算。此类计算存在两大核心缺陷：其一耗时极长，其二仅能通过隐式溶剂化模型考量溶剂化效应。因此，绝大多数计算均在真空环境下进行。然而，近期研究表明，去溶剂化能障、真空堆叠相互作用与结合亲和力三者间存在直接关联，这使得预测难度进一步提升。为克服量子力学计算的上述缺陷，本研究采用神经网络对真空及显式溶剂化环境下与甲苯堆叠的杂芳香化合物开展快速构象优化与分子动力学模拟。研究结果表明，真空环境下得到的能量数据与高水平量子力学计算结果吻合度极佳。此外，本研究证实，显式溶剂化会显著改变杂芳香环的优势取向，这凸显了在药物设计的早期阶段就纳入溶剂化特性的必要性。