This study explores the application and performance of Transformational
Machine Learning (TML) in drug discovery. TML, a meta learning algorithm,
excels in exploiting common attributes across various domains, thus developing
composite models that outperform conventional models. The drug discovery
process, which is complex and time-consuming, can benefit greatly from the
enhanced prediction accuracy, improved interpretability and greater
generalizability provided by TML. We explore the efficacy of different machine
learning classifiers, where no individual classifier exhibits distinct
superiority, leading to the consideration of ensemble classifiers such as the
Random Forest.
Our findings show that TML outperforms base Machine Learning (ML) as the
number of training datasets increases, due to its capacity to better
approximate the correct hypothesis, overcome local optima, and expand the space
of representable functions by combining separate classifiers capabilities.
However, this superiority is relative to the resampling methods applied, with
Near Miss demonstrating poorer performance due to noisy data, overlapping
classes, and nonlinear class boundaries. Conversely, Random Over Sampling (ROS)
provides a more robust performance given its resistance to noise and outliers,
improved class overlap management, and suitability for nonlinear class
boundaries.

该研究探讨了转化机器学习在药物发现中的应用和性能。通过利用各种领域的共同属性，TML（一种元学习算法）发展了优于传统模型的组合模型，从而提供了更高的预测准确性、改善的可解释性和更好的泛化能力。我们还探索了不同的机器学习分类器的有效性，其中没有一个分类器表现出明显的优越性，因此考虑到了像随机森林这样的集成分类器。我们的发现表明，随着训练数据集的增加，TML 在超越传统基础机器学习（ML）方面表现出色，因为它能够更好地逼近正确的假设、克服局部最优解，并通过结合不同分类器的能力来扩展可表示函数的空间。然而，这种优越性相对于所应用的重新采样方法而言，Near Miss 由于嘈杂的数据、重叠的类别和非线性类边界而表现较差。相反，随机过采样（ROS）在噪声和离群值的抵抗能力、改善的类别重叠管理能力和适应非线性类边界方面提供了更稳健的性能。