Abstract：
BACKGROUND: Anthrapyrazoles are a new class of antitumor agents and successors to anthracyclines possessing a broad range of antitumor activity in various model tumors.
OBJECTIVE: The present study introduces novel QSAR models for the prediction of antitumor activity of anthrapyrazole analogues.
METHODS: The predictive performance of four machine learning algorithms, namely artificial neural networks, boosted trees, multivariate adaptive regression splines, and random forest, was studied in terms of variation of the observed and predicted data, internal validation, predictability, precision, and accuracy.
RESULTS: ANN and boosted trees algorithms met the validation criteria. It means that these procedures may be able to forecast the anticancer effects of the anthrapyrazoles studied. Evaluation of validation metrics, calculated for each approach, indicated the artificial neural network (ANN) procedure as the algorithm of choice, especially with regard to the obtained predictability as well as the lowest value of mean absolute error. The designed multilayer perceptron (MLP)-15-7-1 network displayed a high correlation between the predicted and the experimental pIC50 value for the training, test, and validation set. A conducted sensitivity analysis enabled an indication of the most important structural features of the studied activity.
CONCLUSIONS: The ANN strategy combines topographical and topological information and can be used for the design and development of novel anthrapyrazole analogues as anticancer molecules.
OBJECTIVE: The present study introduces novel QSAR models for the prediction of antitumor activity of anthrapyrazole analogues.
METHODS: The predictive performance of four machine learning algorithms, namely artificial neural networks, boosted trees, multivariate adaptive regression splines, and random forest, was studied in terms of variation of the observed and predicted data, internal validation, predictability, precision, and accuracy.
RESULTS: ANN and boosted trees algorithms met the validation criteria. It means that these procedures may be able to forecast the anticancer effects of the anthrapyrazoles studied. Evaluation of validation metrics, calculated for each approach, indicated the artificial neural network (ANN) procedure as the algorithm of choice, especially with regard to the obtained predictability as well as the lowest value of mean absolute error. The designed multilayer perceptron (MLP)-15-7-1 network displayed a high correlation between the predicted and the experimental pIC50 value for the training, test, and validation set. A conducted sensitivity analysis enabled an indication of the most important structural features of the studied activity.
CONCLUSIONS: The ANN strategy combines topographical and topological information and can be used for the design and development of novel anthrapyrazole analogues as anticancer molecules.
摘要：
背景：蒽吡唑是一类新的抗肿瘤剂，是蒽环类抗生素的后继药物，在各种模型肿瘤中具有广泛的抗肿瘤活性。
目的：本研究引入新的QSAR模型来预测蒽吡唑类似物的抗肿瘤活性。
方法：四种机器学习算法的预测性能，即人工神经网络，提升的树木，多元自适应回归样条，和随机森林，根据观测和预测数据的变化进行了研究，内部验证,可预测性,精度,和准确性。
结果：ANN和增强树算法符合验证标准。这意味着这些程序可能能够预测所研究的蒽吡唑的抗癌作用。评估验证指标，为每种方法计算，指出了人工神经网络(ANN)过程作为选择的算法，特别是关于获得的可预测性以及平均绝对误差的最低值。设计的多层感知器（MLP）-15-7-1网络显示出训练的预测和实验pIC50值之间的高度相关性，test,和验证集。进行的敏感性分析可以指示所研究活动的最重要的结构特征。
结论：ANN策略结合了拓扑和拓扑信息，可用于设计和开发作为抗癌分子的新型蒽吡唑类似物。
目的：本研究引入新的QSAR模型来预测蒽吡唑类似物的抗肿瘤活性。
方法：四种机器学习算法的预测性能，即人工神经网络，提升的树木，多元自适应回归样条，和随机森林，根据观测和预测数据的变化进行了研究，内部验证,可预测性,精度,和准确性。
结果：ANN和增强树算法符合验证标准。这意味着这些程序可能能够预测所研究的蒽吡唑的抗癌作用。评估验证指标，为每种方法计算，指出了人工神经网络(ANN)过程作为选择的算法，特别是关于获得的可预测性以及平均绝对误差的最低值。设计的多层感知器（MLP）-15-7-1网络显示出训练的预测和实验pIC50值之间的高度相关性，test,和验证集。进行的敏感性分析可以指示所研究活动的最重要的结构特征。
结论：ANN策略结合了拓扑和拓扑信息，可用于设计和开发作为抗癌分子的新型蒽吡唑类似物。
