  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.
