The computation of tautomer rations of druglike molecules is enormously important in computer-aided drug discovery, as over a quarter of all approved drugs can populate multiple tautomeric species in solution. Unfortunately, accurate calculations of aqueous tautomer ratios---the degree to which these species must be penalized in order to correctly account for tautomers in modeling binding for computer-aided drug discovery---is surprisingly difficult. While quantum chemical approaches to computing aqueous tautomer ratios using continuum solvent models and rigid-rotor harmonic-oscillator thermochemistry are currently state of the art, these methods are still surprisingly inaccurate despite their enormous computational expense. Here, we show that a major source of this inaccuracy lies in the breakdown of the standard approach to accounting for quantum chemical thermochemistry using rigid rotor harmonic oscillator (RRHO) approximations, which are frustrated by the complex conformational landscape introduced by the migration of double bonds, creation of stereocenters, and introduction of multiple conformations separated by low energetic barriers induced by migration of a single proton. Using quantum machine learning (QML) methods that allow us to compute potential energies with quantum chemical accuracy at a fraction of the cost, we show how rigorous alchemical free energy calculations can be used to compute tautomer ratios in vacuum free from the limitations introduced by RRHO approximations. Furthermore, since the parameters of QML methods are tunable, we show how we can train these models to correct limitations in the underlying learned quantum chemical potential energy surface using free energies, enabling these methods to learn to generalize tautomer free energies across a broader range of predictions.

中文翻译：

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使量子机器学习潜能适合实验性自由能数据：预测溶液中的互变异构体比率

在计算机辅助药物发现中，类药物分子的互变异构体比率的计算非常重要，因为所有批准的药物中有超过四分之一可以在溶液中填充多种互变异构体。不幸的是，难以准确计算出水性互变异构体的比率，即为了正确模拟互变异构体在建模结合时必须对这些物种进行惩罚的程度。尽管使用连续溶剂模型和刚性转子谐波振荡器热化学方法来计算水性互变异构体比率的量子化学方法目前是最先进的技术，但尽管它们的计算量巨大，但这些方法仍然出人意料地不准确。这里，我们表明，这种不准确性的主要根源在于使用刚性转子​​谐波振荡器（RRHO）近似值来解释量子化学热化学的标准方法的崩溃，这是由于双键迁移，生成所引入的复杂构象结构而感到沮丧立体中心的引入，以及由单个质子迁移引起的低能垒分隔的多个构象的引入。使用量子机器学习（QML）方法，使我们能够以一小部分成本计算具有量子化学精度的势能，我们展示了如何使用严格的炼金术自由能计算方法来计算真空中的互变异构体比率，而不受RRHO引入的限制近似值。此外，由于QML方法的参数是可调的，