Sadowski J, Kubinyi H
Combinatorial Chemistry and Molecular Modelling, ZHF/G - A 30, BASF AG, D-67056 Ludwigshafen, Germany.
J Med Chem. 1998 Aug 27;41(18):3325-9. doi: 10.1021/jm9706776.
A scoring scheme for the rapid and automatic classification of molecules into drugs and nondrugs was developed. The method is a valuable new tool that can aid in the selection and prioritization of compounds from large compound collections for purchase or biological testing and that can replace a considerable amount of laborious manual work by a more unbiased approach. It is based on the extraction of knowledge from large databases of drugs and nondrugs. The method was set up by using atom type descriptors for encoding the molecular structures and by training a feedforward neural network for classifying the molecules. It was parametrized and validated by using large databases of drugs and nondrugs (169 331 molecules from the Available Chemicals Directory, ACD, and 38 416 molecules from the World Drug Index, WDI). The method revealed features in the molecular descriptors that either qualify or disqualify a molecule for being a drug and classified 83% of the ACD and 77% of the WDI adequately.
开发了一种用于将分子快速自动分类为药物和非药物的评分方案。该方法是一种有价值的新工具，可帮助从大型化合物库中选择化合物并确定其优先级，以便进行购买或生物测试，并且可以通过更无偏见的方法取代大量费力的手工工作。它基于从大型药物和非药物数据库中提取知识。该方法通过使用原子类型描述符对分子结构进行编码，并通过训练前馈神经网络对分子进行分类来建立。它通过使用大型药物和非药物数据库（来自可用化学品目录（ACD）的169331个分子和来自世界药物索引（WDI）的38416个分子）进行参数化和验证。该方法揭示了分子描述符中的特征，这些特征决定了一个分子是否符合药物标准，并且对83%的ACD分子和77%的WDI分子进行了充分分类。
