标题： 基于集成方法的药物设计比较分析 显示英文标题

标题： A Comparative Analysis of the Ensemble Methods for Drug Design

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建模对于药物发现是必要的，但它有许多局限性。 基于集成的机器学习方法已被用于克服这些限制并生成可靠的预测。 集成学习创建一组多样化的模型并将其组合起来。 在我们的比较分析中，每个集成算法都与每个基本算法配对，但基本算法也进行了单独研究。 在这种配置下，在4个不同的数据集上开发和比较了57种算法。 因此，提出了一种复杂集成方法的技术，该技术构建多样化模型并进行整合。 所提出的个体模型作为统一模型没有表现出令人印象深刻的结果，但当组合时被认为是最重要的预测器。 我们评估了集成方法是否总是比单独算法给出更好的结果。 本文中用于获得实验结果的Python代码已上传至Github (https://github.com/rifqat/Comparative-Analysis)。 显示英文摘要

摘要： 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).