Knowledge-aware Synergistic Discovery of Drug Combinations: A Large Language Model Perspective

Abstract

Drug combination therapy with significant advantages is a well-established concept in cancer treatment. Some related efforts have been made with multiple artful deep learning techniques. However, they are usually based on data for drug synergy prediction, ignoring the professional characteristics of data and the systematic knowledge accumulation. Meanwhile, integrating the dispersed professional knowledge and effectively utilizing it in data remains a crucial technical challenge. In this study, we propose KSDDC, a novel model for knowledge-aware synergistic discovery of drug combinations from a large language model (LLM) perspective (i.e., from the continuously learnable and refined large database). Within this framework, three main modules are well-designed, i.e., knowledge-aware drug feature auto-encoding, knowledge-aware cell line feature encoding and drug-drug synergy prediction. Informative embeddings of samples are discovered and combined to make accurate drug synergy prediction. Overall, KSDDC is superior compared with the other shallow machine learning based methods and deep learning based methods on several synergistic prediction benchmarks, where about 19% F1-score improvements over the second best method on DrugComb_1 can be observed. Starting with drug synergy prediction, our studies with knowledge-enabled data mining offer valuable insights and serve as a reference method for future research in this field.