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Prediction of Solvation Free Energies with Thermodynamic Integration Using the General Amber Force Field

机译:利用通用琥珀色力场通过热力学积分预测无溶剂化能量

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Computer-aided drug design (CADD) techniques can be very effective in reducing costs and speeding up drug discovery. The determination of binding and solvation free energies is pivotal for this process and is, therefore, the subject of many studies. In this work, the solvation free energy change (ΔΔG_(solv)) for a total of 92 transformations in small molecules was predicted using Thermodynamic Integration (T1). It was our aim to compare experimental and calculated solvation free energies for typical and prime additions considered in drug optimizations, analyzing trends, and optimizing a T1 protocol. The results showed a good agreement between experimental and predicted values, with an overestimation of the predicted values for CH3, halogens, and NH2, as well as an underestimation for CONH2, but all fall within ±1 keal/mol. NO2 addition showed a larger and systematic underestimation of the predicted ΔΔG_(solv), indicating the need for special attention in these cases, For small molecules, if no experimental data is available, using TI as a theoretical strategy thus appears to be a suitable choice in CADD. It provides a good compromise between time and accuracy.
机译:计算机辅助药物设计(CADD)技术在降低成本和加快药物发现方面非常有效。结合和溶剂化自由能的确定对于该过程至关重要,因此是许多研究的主题。在这项工作中,使用热力学积分(T1)预测了小分子中总共92个转变的溶剂化自由能变化(ΔΔG_(solv))。我们的目的是比较药物优化中考虑的典型添加和主要添加的实验和计算得出的溶剂化自由能,分析趋势并优化T1方案。结果表明,实验值和预测值之间有很好的一致性,CH3,卤素和NH2的预测值被高估,CONH2的估计值被低估,但均在±1 keal / mol之内。添加NO2表示对预测的ΔΔG_(solv)有较大的系统性低估,表明在这些情况下需要特别注意。对于小分子,如果没有可用的实验数据,则使用TI作为理论策略似乎是一个合适的选择。在CADD中。它在时间和准确性之间提供了很好的折衷方案。

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