Abstract
The ability to accurately predict the binding affinities of small organic molecules to biological macromolecules would greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current quantitative physical and empirical modeling approaches to affinity prediction against binding data to biological macromolecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host-guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host-guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from 10 participating groups employing a variety of methods that spanned electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields and explicit solvent models. While empirical models tended to obtain better performance, it was not possible to identify a single approach consistently providing superior predictions across all host-guest systems and statistical metrics, and the accuracy of the methodologies generally displayed a substantial dependence on the systems considered, arguing for the importance of considering a diverse set of hosts in blind evaluations. Several entries exploited previous experimental measurements of similar host-guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it was not able to generated a corresponding improvement of correlation statistics. Comparison to previous rounds of the host-guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement in root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters and improved treatment of chemical effects (e.g., buffer salt conditions, protonation states) may be required to continue to enhance predictive accuracy.
