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理论与实践之间:计算/实验综合方法来了解PROTACs的溶解性和脂性

Andrea Venturi¹, Stefano Di Bona¹, Jenny Desantis¹

¹Department of Chemistry, Biology and Biotechnology, University of Perugia, Via dell' Elce di Sotto 8, Perugia 06123, Italy.

Journal of medicinal chemistry

|September 13, 2024

概括

预测蛋白质分解向仿真体 (PROTACs) 的特性是一项挑战. 这项研究测量了44种PROTAC的动力溶解度和LogD7.4,开发了新的in silico模型,以更好地预测这些关键药物特性.

科学领域:

药物的发现和开发.
药品化学药品化学是一个
计算化学是一种计算化学.

背景情况:

新兴的候选药物越来越多地占据了超越利宾斯基五项规则的化学空间.
针对蛋白质溶解的嵌合体 (PROTACs) 是一个快速增长的治疗类.
由于数据异质性,对PROTAC物理化学性质的准确预测仍然有限.

研究的目的:

测量各种 44 个 PROTAC 的动力溶解度和 LogD7.4.
评估PROTACs现有的in silico预测工具的性能.
开发用于预测PROTAC运动溶解度和LogD7.4.4的新in silico模型.

主要方法:

对44个PROTAC的动力溶解度和LogD7.4的合成和实验测量.
对低溶解度,高脂性化合物的实验协议的优化.
对LogD7.4.4的各种in silico预测工具进行测试和比较.
使用3D结构描述符和实验数据开发新的定量结构与属性关系 (QSPR) 模型.

主要成果:

PROTACs 呈现出一般较低的动力溶解度和高的脂性,需要优化方法.
现有的in silico工具对小分子显示出高精度,但对PROTACs显示出不一致的结果.
开发了新的in silico模型,集成实验数据和3D结构信息.
开发的模型表明,对PROTAC动力溶解度和LogD7.4.4的预测准确度有所提高.

结论:

准确预测PROTAC的物理化学性质对于药物发现至关重要.
现有的计算工具需要对像PROTACs这样的复杂分子进行改进.
开发的in silico模型为预测PROTAC特性提供了一个有希望的方法,有助于未来的药物设计.

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