专利NetML:使用网络科学和机器学习来预测专利中的关键化合物的新型框架
Ting-Fei Zhu1,2, Rong Qian1,2, Xiao Wei1
1Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410003, Hunan, China.
Journal of medicinal chemistry
|January 5, 2024
概括
本研究介绍了PatentNetML,这是一种使用网络科学和机器学习来预测专利中的关键化合物的新框架,有助于药物发现. 它有助于更有效地识别有前途的候选药物.
科学领域:
- 药物的发现和开发.
- 计算化学是一种计算化学.
- 知识产权分析知识产权分析
背景情况:
- 专利对药物研究至关重要,提供早期数据和见解.
- 识别专利中的关键化合物对于发现新型化合物至关重要.
- 现有的方法可能无法充分利用专利数据中的信息.
研究的目的:
- 开发一种创新的方法来预测专利中的关键化合物.
- 为此目的,创建一个强大的框架,整合网络科学和机器学习.
- 通过案例研究来证明拟议框架的实用性.
主要方法:
- 收集了1555项专利和1000个关键化合物的数据集.
- 开发了PatentNetML框架,集成网络科学和机器学习算法.
- 组合网络测量,ADMET属性和物理化学属性用于分类模型.
主要成果:
- 成功构建分类模型以识别关键化合物.
- 证明了PatentNetML在发现专利中隐藏的模式方面的潜力.
- 通过模型解释和案例研究分析展示了框架的能力.
结论:
- 专利网ML为有效识别候选药物提供了一个有希望的基础.
- 该框架有助于加快制药行业的药物发现过程.
- 对于从假定的中央模式中偏离的专利存在已知的限制.
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