Impact of Molecular Representations on Deep Learning Model Comparisons in Drug Response Predictions

Abstract

Deep learning (DL) plays a crucial role in tackling the complexity and heterogeneity of cancer, particularly in predicting drug response. However, the effectiveness of these models is often hindered by inconsistent benchmarks and disparate data sources. To address the gaps in comparisons, we introduce CoMParison workflow for Cross Validation (CMP-CV), an automated cross-validation framework that trains multiple models with user-specified parameters and evaluation metrics. The effectiveness of DL models in predicting drug responses is closely tied to the methods used to represent drugs at the molecular level. In this contribution, we benchmarked commonly leveraged drug representations (graph, molecular descriptors, molecular fingerprints, and SMILES) to lean and understand the predictive capabilities of the models. We compare the ability of different drug representations to encode different structural properties of the drugs by using prediction errors made by models in different drug descriptor domains. We find that, in terms of the average prediction error over the entire test set, molecular descriptor and encoded SMILES representations perform slightly better than the others. However, we also observe that the rankings of the model performance vary in different regions over the descriptor space studied in this work, emphasizing the importance of domain-based model comparison when selecting a model for a specific application. Our efforts are part of CANcer Distributed Learning Environment (CANDLE), enhancing the model comparison capabilities in cancer research and driving the development of more effective strategies for drug response prediction and optimization.