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Abstract：
Chemical liabilities, such as adverse effects and toxicity, play a significant role in modern drug discovery process. In silico assessment of chemical liabilities is an important step aimed to reduce costs and animal testing by complementing or replacing in vitro and in vivo experiments. Herein, we propose an approach combining several classification and chemography methods to be able to predict chemical liabilities and to interpret obtained results in the context of impact of structural changes of compounds on their pharmacological profile. To our knowledge for the first time, the supervised extension of Generative Topographic Mapping is proposed as an effective new chemography method. New approach for mapping new data using supervised Isomap without re-building models from the scratch has been proposed. Two approaches for estimation of model\'s applicability domain are used in our study to our knowledge for the first time in chemoinformatics. The structural alerts responsible for the negative characteristics of pharmacological profile of chemical compounds has been found as a result of model interpretation.
摘要：
化学品负债，如不良反应和毒性，在现代药物发现过程中发挥着重要作用。化学负债的计算机评估是通过补充或替代体外和体内实验来降低成本和动物测试的重要步骤。在这里，我们提出了一种方法，结合了几种分类和化学方法，以便能够预测化学负债，并在化合物结构变化对其药理学特征的影响的背景下解释获得的结果。我们第一次认识到，生成地形图的监督扩展是一种有效的新化学方法。已经提出了使用监督Isomap映射新数据的新方法，而无需从头开始重新构建模型。在我们的研究中，首次在化学信息学中使用了两种方法来估计模型的适用性领域。作为模型解释的结果，已经发现了负责化合物药理学特征负面特征的结构警报。
