合理复合选择的模拟可访问性得分 (AAscore)
Takato Ue1, Akinori Sato1,2, Tomoyuki Miyao1,2
1Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara630-0192, Japan.
Journal of chemical information and modeling
|December 6, 2024
概括
开发了一个新的模拟可访问性评分 (AAscore),使用in silico模型来预测虚拟模拟器. 这一分数有助于在药物发现中优先考虑化合物,但与实验合成的类似物显示出有限的相关性.
科学领域:
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
- 药物发现 药物发现
背景情况:
- 对化合物属性的客观评估在药物发现中至关重要.
- 现有的in silico分数无法捕捉到模拟可访问性.
- 需要可靠的模拟可访问性得分来进行虚拟选和命中到的优化.
研究的目的:
- 提出一种新的模拟可访问性评分 (AAscore) 用于评估化合物合成性.
- 利用回归合成和前产品预测模型来生成虚拟类比.
- 评估AAscore在确定模拟合成化合物的优先级方面的实用性.
主要方法:
- 基于虚拟模拟生成的模拟可访问性评分 (AAscore) 的开发.
- 应用复杂合成预测和前期产品预测模型.
- 使用化合物-核心关系 (CCR) 方法对AAscore与实验合成的类似物进行评估.
- 对AAscore对反应剂数据库大小的敏感性的分析.
主要成果:
- 拟议的AAscore量化了独特的虚拟模拟和合成路由.
- AAscore显示与基于CCR的合成类型的数量有很弱的相关性.
- AAscore受数据库中可用的反应物的数量显著影响.
- 一个案例研究表明,AAscore能够识别可合成的化合物对抗碳酸 anhydrase 2.
结论:
- AAscore 提供了一种新的 in silico 方法来估计模拟可访问性.
- 目前的AAscore需要进一步改进,以准确预测实验模拟合成.
- 优化反应剂数据库和预测模型可以增强AAscore对药物发现工作的预测能力.
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