Tetko I V, Luik A I, Poda G I
Institute of Bioorganic and Oil Chemistry, Ukraine.
J Med Chem. 1993 Apr 2;36(7):811-4. doi: 10.1021/jm00059a003.
We investigated the applications of back propagation artificial neural networks (ANN) for a small dataset analysis in the field of structure-activity relationships. The derivatives of carboquinone were used as an example. It's been found that in this case the use of the same neural network results in unambiguous classification of new molecules. Predictions can be improved with statistical analysis of independent prognosis sets. We suggest that the sign criterion be used as a classification rule. We also compared neural networks with FALS and ALS in leave-one-out prediction. ANN applied to the same dataset has shown the same predictive ability as ALS but poorer than FALS.
我们研究了反向传播人工神经网络（ANN）在构效关系领域中小数据集分析的应用。以卡波醌衍生物为例。结果发现，在这种情况下，使用相同的神经网络可对新分子进行明确分类。通过对独立预后集进行统计分析可以改进预测。我们建议将符号准则用作分类规则。我们还在留一法预测中将神经网络与FALS和ALS进行了比较。应用于同一数据集的ANN显示出与ALS相同的预测能力，但比FALS差。
