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 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.
Highlights
Binding between targets and small molecule drugs depends on a small set of specific interactions (Bissantz et al, 2010)
The set of molecules investigated in this study frequently occurs in drug molecules (Salonen et al, 2011) and has already been investigated in previous publications to characterize their stacking properties using quantum mechanical calculations and molecular mechanics based calculations to estimate their respective solvation properties as monomers as well as complexes (Huber et al, 2014; Bootsma et al, 2019; Loeffler et al, 2019) (Figure 1)
We investigated the stacking interactions of a set of compounds that was recently studied in two publications on a truncated phenylalanine sidechain, i.e., toluene (Bootsma et al, 2019; Loeffler et al, 2020)
Summary
Binding between targets and small molecule drugs depends on a small set of specific interactions (Bissantz et al, 2010). In structure-based drug design, the main goal is to optimize a small molecule to make use of all possible interaction sites provided by the protein’s binding pocket (Bissantz et al, 2010; Kuhn et al, 2011). Certain interactions, e.g., π-π stacking of heteroaromatics, are not properly parametrized in modern force fields to reliably make free energy estimations. These interactions play a major role in drug design (Burley and Petsko, 1985; Meyer et al, 2003; Williams et al, 2003; Adhikary et al, 2019). Stacking can occur as π-π (Huber et al, 2014), halogen-π (Wallnoefer et al, 2010), amide-π (Harder et al, 2013; Bootsma and Wheeler, 2018), cation-π (Gallivan and Dougherty, 1999), and even anion-π (Wheeler and Bloom, 2014) interactions
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
