Accurate force field parameters, potential energy functions, and receptor-ligand models are essential for modeling the solvation and binding of drug-like molecules to a receptor. A large and ever-growing chemical space of medicinally relevant scaffolds has also required these factors, especially force field parameters, to be highly transferable. Generalized force fields such as the CHARMM General Force Field (CGenFF) and the generalized AMBER force field (GAFF) have accomplished this feat along with other contemporaneous ones like OPLS. Here, we analyze the limits in the parametrization of drug-like small molecules by CGenFF and GAFF in terms of the various functional groups represented within them. Specifically, we link the presence of specific functional groups to the error in the absolute hydration free energy of over 600 small molecules, predicted by alchemical free energy methods implemented in the CHARMM program. Our investigation reveals that molecules with (i) a nitro group in CGenFF and GAFF are, respectively, over- or undersolubilized in aqueous medium, (ii) amine groups are undersolubilized more so in CGenFF than in GAFF, and (iii) carboxyl groups are more oversolubilized in GAFF than in CGenFF. We present our analyses of the potential factors underlying these trends. We also showcase the use of a machine-learning-based approach combined with the SHapley Additive exPlanations framework to attribute these trends to specific functional groups, which can be easily adopted to explore the limits of other general force fields.

中文翻译：

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通过预测小分子的水合自由能探索广义 CHARMM 和 AMBER 力场的极限


准确的力场参数、势能函数和受体-配体模型对于模拟药物样分子与受体的溶剂化和结合至关重要。医学相关支架的巨大且不断增长的化学空间也要求这些因素，特别是力场参数，具有高度可转移性。 CHARMM 通用力场 (CGenFF) 和广义 AMBER 力场 (GAFF) 等广义力场与 OPLS 等其他同时代力场一起完成了这一壮举。在这里，我们根据 CGenFF 和 GAFF 所代表的各种官能团来分析类药物小分子参数化的局限性。具体来说，我们将特定官能团的存在与 600 多种小分子的绝对水合自由能误差联系起来，这是通过 CHARMM 程序中实施的炼金自由能方法预测的。我们的研究表明，(i) CGenFF 和 GAFF 中带有硝基的分子在水性介质中分别过度溶解或溶解不足，(ii) 胺基在 CGenFF 中比在 GAFF 中溶解更不足，(iii) 羧基在 GAFF 中比在 CGenFF 中过度溶解。我们对这些趋势背后的潜在因素进行了分析。我们还展示了使用基于机器学习的方法与 SHapley Additive exPlanations 框架相结合，将这些趋势归因于特定的功能组，可以轻松地采用该方法来探索其他一般力场的极限。