Vukovic Kristijan, Gadaleta Domenico, Benfenati Emilio
Istituto di Ricerche Farmacologiche Mario Negri-IRCCS, Via Mario Negri 2, 20156, Milan, Italy.
Jozef Stefan International Postgraduate School, Jamova cesta 39, 1000, Ljubljana, Slovenia.
J Cheminform. 2019 Apr 3;11(1):27. doi: 10.1186/s13321-019-0350-y.
Several QSAR methodology developments have shown promise in recent years. These include the consensus approach to generate the final prediction of a model, utilizing new, advanced machine learning algorithms and streamlining, standardization and automation of various QSAR steps. One approach that seems under-explored is at-the-runtime generation of local models specific to individual compounds. This approach was quite likely limited by the computational requirements, but with current increases in processing power and the widespread availability of cluster-computing infrastructure, this limitation is no longer that severe.
We propose a new QSAR methodology: aiQSAR, whose aim is to generate endpoint predictions directly from the input dataset by building an array of local models generated at-the-runtime and specific for each compound in the dataset. The local group of each compound is selected on the basis of fingerprint similarities and the final prediction is calculated by integrating the results of a number of autonomous mathematical models. The method is applicable to regression, binary classification and multi-class classification and was tested on one dataset for each endpoint type: bioconcentration factor (BCF) for regression, Ames test for binary classification and Environmental Protection Agency (EPA) acute rat oral toxicity ranking for multi-class classification. As part of this method, the applicability domain of each prediction is assessed through the applicability domain measure, calculated on the basis of the fingerprint similarities in each local group of compounds.
We outline the methodology for a new QSAR-based predictive tool whose advantages are automation, group-specific approach to modelling and simplicity of execution. Our aim now will be to develop this method into a stand-alone software tool. We hope that eventual adoption of our tool would make QSAR modelling more accessible and transparent. Our methodology could be used as an initial modelling step, to predict new compounds by simply loading the training dataset as an input. Predictions could then be further evaluated and refined either by other tools or through optimization of aiQSAR parameters.
近年来，几种定量构效关系（QSAR）方法的发展显示出了前景。这些包括用于生成模型最终预测的共识方法，利用新的、先进的机器学习算法以及简化、标准化和自动化各种QSAR步骤。一种似乎未被充分探索的方法是在运行时生成特定于单个化合物的局部模型。这种方法很可能受到计算要求的限制，但随着当前处理能力的提高和集群计算基础设施的广泛可用，这种限制不再那么严重。
我们提出了一种新的QSAR方法：aiQSAR，其目的是通过构建在运行时生成的、特定于数据集中每个化合物的局部模型数组，直接从输入数据集中生成终点预测。每个化合物的局部组基于指纹相似性进行选择，最终预测通过整合多个自主数学模型的结果来计算。该方法适用于回归、二元分类和多类分类，并针对每种终点类型在一个数据集上进行了测试：用于回归的生物富集因子（BCF）、用于二元分类的艾姆斯试验以及用于多类分类的美国环境保护局（EPA）急性大鼠经口毒性排名。作为该方法的一部分，通过基于每个局部化合物组中的指纹相似性计算的适用性域度量来评估每个预测的适用性域。
我们概述了一种基于QSAR的新预测工具的方法，其优点是自动化、针对建模的组特异性方法和执行的简单性。我们现在的目标是将该方法开发成一个独立的软件工具。我们希望最终采用我们的工具将使QSAR建模更易于使用和透明。我们的方法可以用作初始建模步骤，通过简单地加载训练数据集作为输入来预测新化合物。然后可以通过其他工具或通过优化aiQSAR参数来进一步评估和完善预测。
