人工智能赋能癌症协同药物组合预测的现状与挑战  

Current status and challenges of artificial intelligence-enabled prediction of synergistic cancer drug combinations

作　　者：谭洪 林圣庚 熊毅 TAN Hong;LIN Shenggeng;XIONG Yi(School of Life Sciences and Biotechnology,Shanghai Jiao Tong University,Shanghai 200240,China;Artificial Intelligence Biomedical Center,Zhangjiang Institute for Advanced Study,Shanghai Jiao Tong University,Shanghai 201203,China)

机构地区：[1]上海交通大学生命科学技术学院,上海200240 [2]上海交通大学张江高等研究院人工智能生物医药中心,上海201203

出　　处：《中国癌症杂志》2024年第9期807-813,共7页China Oncology

基　　金：国家重点研发计划重点专项(2023YFC2506400);国家自然科学基金面上项目(62172274)。

摘　　要：近年来,癌症的发病率和死亡率不断攀升,耐药性已成为目前癌症治疗的重大挑战。传统的单一化治疗方法并不能有效地应对肿瘤细胞的异质性及其多重耐药,常导致疗效不理想。药物联合治疗作为一种重要的治疗策略,可通过多药协同作用提高疗效并延缓耐药性的发展。然而,传统的药物组合筛选方法耗时且成本高昂。随着数据的积累和计算方法的发展,人工智能特别是深度学习在癌症协同药物组合预测中展现出巨大潜力。通过人工智能技术,研究人员可以更高效地预测药物组合是否存在协同作用,降低实验成本,发现新的潜在协同药物组合。然而,人工智能模型仍存在可解释性差、特征融合不充分及标注数据缺乏等问题。本文就人工智能在癌症药物组合协同作用预测中的应用进展予以综述。首先,介绍药物耐药机制及联合治疗的挑战,指出传统方法在药物组合筛选中的局限性。然后,介绍不同深度学习模型在癌症协同药物组合预测中的优缺点,包括前馈神经网络、图神经网络、自编码器、可见神经网络、Transformer及其扩展模型等。针对现有深度学习模型普遍存在的问题,本文提出了解决方案,包括利用多模态数据增强模型的泛化能力,采用迁移学习和多任务学习应对数据不足问题,以及设计更具可解释性的模型以推动临床应用。未来,药物协同组合预测领域有望通过开发标准化的协同指标、提升模型的可解释性、整合多模态数据及应对数据稀缺问题,进一步推动模型从实验室研究走向临床应用,从而为癌症治疗提供更有效的解决方案。In recent years,the incidence and mortality rates of cancer have been rising steadily,with drug resistance becoming major challenge in cancer treatment.Traditional monolithic treatment approaches have proven ineffective in addressing the heterogeneity of tumor cells and their multiple resistance mechanisms,leading to suboptimal therapeutic outcomes.Drug combination therapy,as a critical strategy,aims to enhance efficacy and delay the development of drug resistance through the synergistic action of multiple drugs.However,conventional methods for screening drug combinations are time-consuming and costly.With the accumulation of data and advances in computational methods,artificial intelligence,particularly deep learning,has demonstrated great potential in predicting synergistic drug combinations for cancer treatment.Artificial intelligence technologies allow researchers to efficiently predict whether drug combinations exhibit synergistic effects,reducing experimental costs and identifying novel potential synergistic combinations.Nevertheless,artificial intelligence models still face challenges such as poor interpretability,insufficient feature integration,and a lack of labeled data.This paper provided a comprehensive review of the advancements in artificial intelligence applications for predicting synergistic drug combinations in cancer therapy.First,it discussed the mechanisms of drug resistance and the challenges of combination therapy,highlighting the limitations of traditional drug combination screening methods.Then,it presented the advantages and disadvantages of various deep learning models used for predicting synergistic drug combinations,including feedforward neural networks,graph neural networks,autoencoders,visible neural networks,Transformer and their extended models.In response to the common issues in current deep learning models,this review proposed several solutions,such as leveraging multimodal data to enhance model generalization,employing transfer learning and multitask learning to address data sca

关 键 词：癌症 协同药物组合 人工智能 深度学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]