Binding thermodynamics of host-guest systems with SMIRNOFF99Frosst from the Open Force Field Group

Abstract

Designing ligands that bind their target with high affinity and specificity is a key step in small-molecule drug discovery. Yet accurate predictions of protein-ligand binding free energies are difficult and errors in the calculations can be traced to challenges adequately sampling conformational space, ambiguous protonation states, and other causes. Noncovalent complexes between a cavity-containing host molecule and drug-like guest molecules have emerged as powerful tools for modeling protein-ligand binding. Due to their small size and extensive experimental characterization, calculations of host-guest binding free energies, enthalpies, and entropies offer an opportunity to directly probe, and ultimately optimize, force fields. The Open Force Field Initiative aims to create a modern, open software infrastructure for automatically generating and validating force fields using high-quality data sets. The first force field to arise out of this effort, named SMIRNOFF99Frosst, has one tenth the number of parameters of a typical general small molecule force field, such as GAFF, yet predicts binding thermodynamics that are on average, at least as accurate. Here, we report the results of free energy calculations on 43 α and β-cyclodextrin host-guest pairs for which experimental data are available. Our calculations were performed using the attach-pull-release method as implemented in the open source package, pAPRika. On binding free energies, the root mean square error of the predictions relative to experiment is 0.82 kcal/mol for SMIRNOFF99Frosst and 1.58 kcal/mol for GAFF version 2.1. These results suggest significant room for improvement in force fields, and will help create a transparent and robust method of evaluating future candidate parameter sets from the Open Force Field Group. Improving the performance of force fields for predicting binding affinities will help reduce the timescale and cost required to generate drug candidates.

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