动力预测的解剖学,重点关注具有越来越大的动力差异的结构类型,包括活动悬崖
Tiago Janela1, Jürgen Bajorath1,2
1Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, D-53115 Bonn, Germany.
准确的化合物功效预测是具有挑战性的,特别是对于活动悬崖 (ACs). 使用匹配分子对 (MMP) 的新测试系统显示,预测错误随着强度差异的增加而增加,突出了药物设计的局限性.
科学领域:
- 计算化学是一种计算化学.
- 化学信息学 化学信息学
- 机器学习在药物发现中的作用
背景情况:
- 强度预测对于化合物设计至关重要,但面临局限性.
- 活动悬崖 (ACs),具有很大的强度差异的结构类型,对预测模型构成重大挑战.
研究的目的:
- 开发和验证一种用于预测化合物功效的新型测试系统,特别是针对活动悬崖.
- 分析关于模拟对内的不同功率差异的预测准确性.
主要方法:
- 分隔匹配的分子对 (MMP) 用于创建一个测试系统来监测预测准确性.
- 在基于MMP的数据集上使用各种机器学习和控制方法进行了系统的预测.
- 沙普利价值分析是一种可解释的AI方法,用于识别影响预测的关键结构特征.
主要成果:
- 随着训练和测试化合物之间的强度差异增加,预测错误显著增加,特别是在活动悬崖上.
- 全球预测准确度被具有小强度差异的模拟对的流行所掩盖,这些对被准确预测.
- 沙普利的价值分析显示,MMP合作伙伴之间共享或缺少的结构特征影响了预测,解释了类似类型的准确预测和AC的故障.
结论:
- 开发的基于MMP的测试系统有效地突出了当前强度预测方法的局限性,特别是对于活动悬崖.
- 预测准确性对结构相关化合物之间的强度差异非常敏感.
- 可解释的人工智能方法对于了解药物发现中预测成功和失败的结构基础是有价值的.
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