Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States.
Department of Macromolecular Science and Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States.
J Chem Inf Model. 2024 May 27;64(10):4089-4101. doi: 10.1021/acs.jcim.4c00126. Epub 2024 May 8.
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.
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。具有治疗相关性的大量且不断增长的化学空间也需要这些因素,特别是力场参数具有高度可转移性。通用力场,如 CHARMM 通用力场(CGenFF)和广义 AMBER 力场(GAFF),以及同时期的其他力场,如 OPLS,都实现了这一目标。在这里,我们根据它们所代表的各种官能团,分析 CGenFF 和 GAFF 对药物样小分子进行参数化的局限性。具体来说,我们将特定官能团的存在与通过 CHARMM 程序中实施的热力学自由能计算方法预测的 600 多个小分子的绝对水合自由能的误差联系起来。我们的研究表明,(i)CGenFF 和 GAFF 中的硝基官能团使分子分别在水介质中过度或欠溶解,(ii)胺官能团在 CGenFF 中比在 GAFF 中欠溶解更严重,(iii)羧基官能团在 GAFF 中比在 CGenFF 中过溶解更严重。我们提出了对这些趋势背后潜在因素的分析。我们还展示了一种基于机器学习的方法与 SHapley Additive exPlanations 框架的结合使用,将这些趋势归因于特定的官能团,这可以很容易地应用于探索其他通用力场的局限性。