Cross-HPO: Optimizing Neural Networks for Cancer Drug Response Using Hyperparameter Tuning on Multiple Pharmacogenomic Datasets

Rajeev Jain, Justin M Wozniak, Alexander Partin, Andreas Wilke, Yitan Zhu, Priyanka Vasanthakumari, Oleksandyr Narykov, Jamie Overbeek, Rylie Weaver, Chen Wang, Yuanhang Liu, M. Ryan Weil, Thomas Brettin, Rick L. Stevens

Abstract:

Predicting and comparing anti-cancer drug responses using deep learning models across datasets is a challenging
modern problem. In this study, we evaluated and optimized hyperparameters in several novel neural network-based
models, including GraphDRP [1], IGTD [2], Paccmann [3], PathDSP [4], and HiDRA [5], and a machine learning
model LGBM (build with LightGBM), across multiple public pharmacogenomic datasets: CCLE [6], CTRPv2 [7],
gCSI [8], GDSCv1 [9], and GDSCv2 [10]. Our primary objective was to enhance prediction performance and
robustness through hyperparameter optimization (HPO) tailored to each dataset. As a result, we have published the
HPO framework and HPO results on GitHub for the research community and have started the cross-analysis of these
HPO runs. The results are a first effort in the cross-model, cross-dataset HPO analysis (termed “Cross-HPO”)
that is now possible. We believe this work would lead to better drug discovery candidate evaluation and increase the
success rate of discovery.