Minisymposium Presentation

In the evolving field of computational drug design and discovery, the accurate prediction of drug responses through deep learning models remains a significant challenge due to varying methodologies in model implementation and validation. This inconsistency hampers the objective assessment of model capabilities across different drug representation methods, architectures, and datasets. As models become more complex and datasets more diverse, the necessity for standardized model comparison methodologies becomes imperative. Traditional comparison approaches, which typically rely on performance scores from disparate studies, lead to incomparable and inconsistent results, obstructing the understanding of factors critical to predictive performance. Addressing this issue, I will discuss our results based on foundaitonal models and large scale CMP-CV workflow, an automated cross-validation framework designed for the consistent training and evaluation of multiple deep-learning models. By employing standardized datasets, preprocessing techniques, and performance metrics, CMP-CV fosters controlled experimentation while allowing systematic variation in model hyperparameters and architectures. Additionally, the framework supports custom analytical functions, enabling a more profound investigation into model representations and associated uncertainties, thereby establishing a more standardized and comprehensive approach to model comparison in drug response prediction.