CDFA: calibrated deep feature aggregation for screening synergistic drug combinations

Xiaorui Kang¹XIAOYAN LIU²Quan Zou^1,3Tiantian Li^4*Ximei Luo^3,5*

• ¹Faculty of Applied Sciences, Macao Polytechnic University, Macao, Macau Region, China
• ²Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang Province, China
• ³University of Electronic Science and Technology of China, Chengdu, China
• ⁴Editorial Office, Geriatric Hospital of Nanjing Medical University, Nanjing, China
• ⁵Yangtze River Delta Research Institute, University of Electronic Science and Technology of China, Quzhou, Zhejiang Province, China

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. 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. Experiments results have demonstrated that CDFA outperforms existing state-of-the-art deep learning models.