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热力学指导的先导物发现和优化。

Thermodynamics guided lead discovery and optimization.

机构信息

Sanofi-Aventis CHINOIN, 1-5. Tó u. Budapest, Hungary, H-1045, Hungary.

出版信息

Drug Discov Today. 2010 Nov;15(21-22):919-32. doi: 10.1016/j.drudis.2010.08.013. Epub 2010 Aug 27.

Abstract

The documented unfavorable changes of physicochemical properties during lead discovery and optimization prompted us to investigate the present practice of medicinal chemistry optimization from a thermodynamic perspective. Basic principles of binding thermodynamics suggest that discriminating between enthalpy-driven and entropy-driven optimizations could be beneficial. We hypothesize that entropy-driven optimizations might be responsible for the undesirable trend observed in physicochemical properties. Consequently, we suggest that enthalpy-driven optimizations are preferred because they provide better quality compounds. Monitoring binding thermodynamics during optimization programs initiated from thermodynamically characterized hits or leads, therefore, could improve the success of discovery programs. Here, we summarize common industry practices for tackling optimization challenges and review how the assessment of binding thermodynamics could support medicinal chemistry efforts.

摘要

在发现和优化铅的过程中,理化性质出现了不利变化,这促使我们从热力学角度研究当前的药物化学优化实践。结合热力学的基本原理表明,区分焓驱动和熵驱动的优化可能是有益的。我们假设熵驱动的优化可能是导致理化性质观察到的不良趋势的原因。因此,我们建议优先进行焓驱动的优化,因为它们可以提供更好质量的化合物。在从热力学特征明确的命中或先导物开始的优化项目中监测结合热力学,可能会提高发现项目的成功率。在这里,我们总结了常见的行业实践,以应对优化挑战,并回顾了评估结合热力学如何支持药物化学工作。

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