CDFA: Calibrated deep feature aggregation for screening synergistic drug combinations.

Introduction: Drug combination therapy represents a promising strategy for addressing complex diseases, offering the potential for improved efficacy while mitigating safety concerns. However, conventional wet-lab experimentation for identifying optimal drug combinations is resource-intensive due to the vast combinatorial search space. To address this challenge, computational methods leveraging machine learning and deep learning have emerged to effectively navigate this space. Methods: In this study, we introduce a Calibrated Deep Feature Aggregation (CDFA) framework for screening synergistic drug combinations. Concretely, CDFA utilizes a novel cell line representation based on the protein information and gene expression capturing complementary biological determinants of drug response. Besides, a novel feature aggregation network is proposed based on the Transformer to model the intricate interactions between drug pairs and cell lines through multi-head attention mechanisms, enabling discovery of non-linear synergy patterns. Furthermore, a method is introduced to quantify and calibrate the uncertainties associated with CDFA's predictions, enhancing the reliability of the identified synergistic drug combinations. Results: Experiments results have demonstrated that CDFA outperforms existing state-of-the-art deep learning models. Discussion: The superior performance of CDFA stems from its biologically informed cell line representation, its ability to capture complex non-linear drug-cell interactions via attention mechanisms, and its enhanced reliability through uncertainty calibration. This framework provides a robust computational tool for efficient and reliable drug combination screening.