人工智能赋能癌症协同药物组合预测的现状与挑战

谭洪; 林圣庚; 熊毅

doi:10.19401/j.cnki.1007-3639.2024.09.001

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人工智能赋能癌症协同药物组合预测的现状与挑战

专家述评 | 更新时间：2025-12-31

- 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
- 中国癌症杂志 2024年34卷第9期页码：807-813
- 作者机构：
  
  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
- 作者简介：
  
  [ "谭洪（0009-0008-8030-6670），硕士。" ]
  [ "熊毅，研究员，博士研究生导师。教育部“长江学者奖励计划”青年学者，上海市计算机学会生物信息学专业委员会秘书长。聚焦于人工智能驱动的生物大分子功能预测与设计、药物发现及疫苗设计等研究方向。主持多项国家重点研发计划课题和国家自然科学基金面上项目。以通信作者在Nature Machine Intelligence等SCI收录期刊上发表论文40余篇。" ]
- 基金信息：
  
  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)
- DOI：10.19401/j.cnki.1007-3639.2024.09.001
  中图分类号： TP181
- 收稿：2024-08-07，
  
  修回：2024-09-13，
  
  纸质出版：2024-09-30
- 稿件说明：
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谭洪, 林圣庚, 熊毅. 人工智能赋能癌症协同药物组合预测的现状与挑战[J]. 中国癌症杂志, 2024,34(9):807-813.

Hong TAN, Shenggeng LIN, Yi XIONG. Current status and challenges of artificial intelligence-enabled prediction of synergistic cancer drug combinations[J]. China Oncology, 2024, 34(9): 807-813.
谭洪, 林圣庚, 熊毅. 人工智能赋能癌症协同药物组合预测的现状与挑战[J]. 中国癌症杂志, 2024,34(9):807-813. DOI： 10.19401/j.cnki.1007-3639.2024.09.001.

Hong TAN, Shenggeng LIN, Yi XIONG. Current status and challenges of artificial intelligence-enabled prediction of synergistic cancer drug combinations[J]. China Oncology, 2024, 34(9): 807-813. DOI： 10.19401/j.cnki.1007-3639.2024.09.001.

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。

Abstract

In recent years

the incidence and mortality rates of cancer have been rising steadily

with drug resistance becoming a 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 scarcity

and designing more interpretable models to facilitate clinical application. In the future

the field of synergistic drug combination prediction is expected to benefit from the development of standardized synergy metrics

improvements in model interpretability

integration of multimodal data

and effective handling of data limitations

further advancing the transition of models from laboratory research to clinical practice

ultimately providing more effective solutions for cancer treatment.

关键词

Keywords

references

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