论文标题
对药物设计的合奏方法的比较分析
A Comparative Analysis of the Ensemble Methods for Drug Design
论文作者
论文摘要
定量结构活性关系（QSAR）是一种计算机建模技术，用于识别化学化合物的结构特性与生物活性之间的关系。 QSAR建模是药物发现所必需的，但它有许多局限性。基于合奏的机器学习方法已用于克服局限性并产生可靠的预测。合奏学习创建了一组不同的模型并将它们结合在一起。在我们的比较分析中，将每种集合算法与每个基本算法配对，但也分别研究了基本算法。在此配置中，开发了57个算法并在4个不同的数据集上进行了比较。因此，提出了一种复杂合奏方法的技术来构建多元化的模型并将其集成。提出的单个模型并未显示出令人印象深刻的结果作为统一模型，但合并后被认为是最重要的预测因子。我们评估了合奏是否总是比单个算法更好。为了获得本文实验结果的Python代码已上传到GitHub（https://github.com/rifqat/comparative-analysis）。
Quantitative structure-activity relationship (QSAR) is a computer modeling technique for identifying relationships between the structural properties of chemical compounds and biological activity. QSAR modeling is necessary for drug discovery, but it has many limitations. Ensemble-based machine learning approaches have been used to overcome limitations and generate reliable predictions. Ensemble learning creates a set of diverse models and combines them. In our comparative analysis, each ensemble algorithm was paired with each of the basic algorithms, but the basic algorithms were also investigated separately. In this configuration, 57 algorithms were developed and compared on 4 different datasets. Thus, a technique for complex ensemble method is proposed that builds diversified models and integrates them. The proposed individual models did not show impressive results as a unified model, but it was considered the most important predictor when combined. We assessed whether ensembles always give better results than individual algorithms. The Python code written to get experimental results in this article has been uploaded to Github (https://github.com/rifqat/Comparative-Analysis).
