Accurately predicting the binding affinities of small organic molecules to biological macromolecules can 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 computational 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 ten participating groups employing a variety of methods that spanned from electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields with explicit solvent models. While empirical models tended to obtain better performance than first-principle methods, it was not possible to identify a single approach that consistently provided superior results across all host-guest systems and statistical metrics. Moreover, the accuracy of the methodologies generally displayed a substantial dependence on the system considered, emphasizing the need for host diversity 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 did not correspond to an improvement in statistical correlation. 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 regarding root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., buffer salt conditions, protonation states), may be required to further enhance predictive accuracy.

中文翻译：

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SAMPL6主机-来宾绑定亲和力预测挑战概述。

准确预测小有机分子与生物大分子的结合亲和力可以通过减少必须合成的化合物数量来大大加速药物开发，以实现所需的效能和选择性。不幸的是，评估诸如结合构象动力学缓慢，可滴定组数众多以及缺乏高质量的盲数据集等挑战使评估当前针对亲和力预测数据与生物大分子的结合力的准确性的过程受挫。在过去的SAMPL盲目的挑战练习中，来宾-来宾系统已经成为一种实用有效的方法，可以在评估当前数量化建模工具的预测性能时规避这些挑战，同时仍提供能够拥有紧密绑定亲和力的系统。在这里，我们概述了SAMPL6宿主-客体结合亲和力预测挑战，该挑战具有三个超分子宿主：八酸（OA），密切相关的四-内-甲基-八酸（TEMOA）和葫芦[8] ] uril（CB8），以及21个有机小客体分子。从十个参与小组中采用了多种方法，共收到119个条目，这些方法从隐式溶剂中的电子结构和可移动类型计算到使用显式溶剂模型的经验力场的炼金术和平均力策略的潜力。虽然经验模型倾向于比第一原理方法获得更好的性能，不可能找到一种单一的方法来在所有来宾-来宾系统和统计指标之间持续提供卓越的结果。此外，这些方法的准确性通常显示出对所考虑系统的实质依赖，强调了盲目评估中对主机多样性的需求。一些条目利用了以前类似主机系统的实验测量结果，以通过某种形式的基础机器学习来改善其基于物理的预测。尽管此策略成功地减少了系统错误，但并没有带来统计相关性的改善。与以前的几轮对宾客自由结合能量挑战的比较表明，通过OA和TEMOA系统的亲和力预测，相关性得到了整体改善，但是在过去几轮挑战中，关于均方根误差的改进令人惊讶地缺乏。数据表明，可能需要进一步完善力场参数，以及改进对化学作用的处理（例如，缓冲盐条件，质子化状态），以进一步提高预测准确性。