基于拓和基于对应的诱数据库:用于训练和基准测试机器学习评分函数的公正在线数据库
Xujun Zhang1, Chao Shen1, Tianyue Wang1
1Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Journal of medicinal chemistry
|June 15, 2023
概括
我们开发了基于拓和基于对应的诱数据库 (ToCoDDB),这是基于机器学习的评分函数的最大的公正数据集. ToCoDDB有助于准确的结合亲和力预测和基于结构的虚拟选.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 基于机器学习的评分函数 (MLSF) 显示了增强绑定亲和力预测和基于结构的虚拟选 (SBVS) 的前景.
- 准确的MLSF开发需要大型,多样化和公正的数据集,而这些数据集目前缺乏.
- 现有的数据集往往含有隐藏的偏见和不足的数据,阻碍了MLSF的性能.
研究的目的:
- 开发一个新的,大规模的,公正的诱数据库,用于在SBVS中培训和评估MLSF.
- 解决现有数据集在规模,多样性和偏差方面的局限性.
- 为计算化学和药物发现社区提供有价值的资源.
主要方法:
- 从科学文献和已建立的数据库中收集了生物标和活性连接物.
- 使用条件循环神经网络和分子对接技术,生成和放弃诱.
- 编制了基于拓和基于形状的诱数据库 (ToCoDDB).
主要成果:
- 到目前为止,ToCoDDB是迄今为止最大的公正诱数据库,包含155个目标的240万个诱.
- 该数据库为每个目标提供了详细的信息和绩效基准,促进了MLSF的开发.
- 实现了在线诱生成功能,扩大了数据库的实用性.
结论:
- 对于SBVS,ToCoDDB为开发和验证准确的MLSF提供了显著的进步.
- 数据库及其在线生成工具将加速药物发现工作.
- ToCoDDB是免费访问的,促进在计算药物设计的更广泛的研究和应用.
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