FitScore:基于快速机器学习的3D虚拟查丰富度得分
Daniel K Gehlhaar1, Daniel J Mermelstein2
1Pfizer, Inc., 10777 Science Center Drive, San Diego, CA, 92121, USA. dan.gehlhaar@pfizer.com.
Journal of computer-aided molecular design
|August 16, 2024
概括
一个新的FitScore功能改善了用于药物发现的虚拟查丰富性. 这种基于知识的评分方法增强了联体受体兼容性预测,减少了大型复合物库选中的错误阳性.
科学领域:
- 计算化学的计算化学
- 化学信息学 化学信息学
- 药物发现 药物发现 药物发现
背景情况:
- 虚拟查对于药物发现至关重要,但面临着大型复合数据库和高成本的挑战.
- 当前的快速对接方法往往会损害得分准确度,导致缩不良和许多假阳性.
研究的目的:
- 开发一个新的评分功能,FitScore,以提高虚拟查丰富度.
- 为了提高预测在计算药物发现中的联体受体兼容性的准确性.
主要方法:
- 开发了一个两部分的评分功能,将基于知识的组件与可调节的权重矩阵结合起来.
- 基于知识的组件预测受体环境中的原子类型概率.
- 该FitScore将这些概率转换为虚拟选的无维分数.
主要成果:
- 在标准化对接测试集中,FitScore显示了高度的丰富性.
- 评分功能有效地代表了带结合部位的兼容性.
- 改进的丰富度可以减少虚拟查结果中的错误阳性.
结论:
- 在计算化学的虚拟选丰富方面,FitScore提供了显著的进展.
- 这种方法解决了对大型复合图书馆进行具有成本效益和准确选的需求.
- "FitScore"是加速药物发现管道的宝贵工具.
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