Instituto de Química, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Instituto de Química, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Adv Protein Chem Struct Biol. 2018;113:85-117. doi: 10.1016/bs.apcsb.2018.04.001. Epub 2018 May 4.
The steps followed in the knowledge discovery in databases (KDD) process are well documented and are widely used in different areas where exploration of data is used for decision making. In turn, while different workflows for developing quantitative structure-activity relationship (QSAR) models have been proposed, including combinatorial use of QSAR, there is now agreement on common requirements for building trustable predictive models. In this work, we analyze and confront the steps involved in KDD and QSAR and present how they comply with the OECD principles for the validation, for regulatory purposes, of QSAR models.
在数据库中的知识发现(KDD)过程中遵循的步骤有详细的记录,并在数据探索用于决策的不同领域得到广泛应用。反过来,虽然已经提出了用于开发定量构效关系(QSAR)模型的不同工作流程,包括 QSAR 的组合使用,但现在已经就建立可信预测模型的共同要求达成一致。在这项工作中,我们分析和面对 KDD 和 QSAR 所涉及的步骤,并展示它们如何符合 OECD 关于 QSAR 模型验证的原则,以用于监管目的。