开发预测能力,通过生物仿真染色学对生理化学性质进行高通量查
Damian Tuz1,2, Damian Smuga1, Tomasz Pawiński2
1Laboratory of Physicochemical Analysis, Department of Medicinal Chemistry, Celon Pharma S.A., Marymoncka 15, 05-152 Kazuń Nowy, Poland.
计算模型和机器学习通过预测化合物特性来加速药物发现. 这种方法使用生物模拟色谱和in silico数据来更快地选新的化学实体.
科学领域:
- 计算化学是一种计算化学.
- 药物的发现和开发.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 越来越高的体外和体内测试成本需要用于早期药物发现的计算模型.
- 高通量选对于生成关于新化学化合物的全面数据至关重要.
研究的目的:
- 审查用于评估药理动力学和药理动力学性质的创新计算方法.
- 突出机器学习作为药物开发中的变革性分析工具的整合.
主要方法:
- 利用机器学习算法来训练预测模型.
- 将生物模拟色谱数据与in silico分子特征/指纹结合起来.
- 利用已知化合物的体内数据来预测新化学实体的特性.
主要成果:
- 机器学习模型可以有效地预测新化学实体的体内数据.
- 生物仿真色谱作为物理化学试验的高通量替代品.
- 多种数据源的整合提高了预测准确度.
结论:
- 计算策略,特别是机器学习,显著加快了复合图书馆选.
- 这些方法提供了简化药物开发过程的实用方法.
- 该评论提供了对预测性表征的先进计算工具的见解.
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