计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 17-21.doi: 10.11896/jsjkx.210400150
康雁, 徐玉龙, 寇勇奇, 谢思宇, 杨学昆, 李浩
KANG Yan, XU Yu-long, KOU Yong-qi, XIE Si-yu, YANG Xue-kun, LI Hao
摘要: 药物相互作用的不良反应已经成为消化系统疾病、心血管疾病等发病率升高的重要原因之一,并且导致药物退出市场,因此准确预测药物相互作用受到了广泛关注。针对传统Encoder-Decoder模型无法捕捉药物子结构之间依赖的问题,提出了基于Transformer和LSTM的药物相互作用预测模型TransDDI(TransformerDDI)。TransDDI包括数据预处理模块、潜在特征抽取模块和映射模块3部分。数据预处理模块利用SPM算法从药物的SMILES格式输入中提取出表征药物的频繁子结构,形成药物特征向量,进而生成药物对的特征向量。潜在特征抽取模块利用Transformer充分挖掘特征向量中子结构之间蕴含的信息,突出不同子结构的不同重要作用,生成潜在特征向量。映射模块主要是将药物对的潜在特征向量和数据库中频繁子结构的向量形成字典表示,并且利用融合了LSTM的神经网络进行预测。在真实数据集BIOSNAP和DrugBank上,将所提模型与另外6种机器学习、深度学习方法进行实验比较。结果显示,TransDDI准确率更高,便于药物相互作用预测。
中图分类号:
[1] FOUCQUIER J,GUEDJ M.Analysis of drug combinations:currentmethodological landscape[J].Pharmacology Research & Perspectives,2015,3(3):e00149. [2] EDWARDS I R,ARONSON J K.Adverse drug reactions:definitions,diagnosis,and management[J].Lancet,2000,356(9237):1255-1259. [3] TATONETTI N P,HASKIN F G,ALTMAN R B.A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports[J].Journal of the American Medical Informatics Association,2011(1):1. [4] PIRMOHAMED M.Adverse drug reactions as cause of admission to hospital:prospective analysis of 18 820 patients[J].BMJ,2004,329(7456):15-19. [5] QATO D M,ALEXANDER G C,CONTI R M,et al.Use ofPrescription and Over-the-counter Medications and Dietary Supplements Among Older Adults in the United States[J].Jama,2008,300(24):2867-2878. [7] ONAKPOYA I J,HENEGHAN C J,ARONSON J K.Post-marketing withdrawal of 462 medicinal products because of adverse drug reactions:a systematic review of the world literature[J].Bmc Medicine,2016,14(1):10. [8] VARNEK A,BASKIN I.Machine Learning Methods for Pro-perty Prediction in Chemoinformatics:Quo Vadis?[J].Journal of Chemical Information & Modeling,2012,52(6):1413-1437. [9] NEUGEBAUER A,HARTMANN R W,KLEIN C D.Prediction of protein-protein interaction inhibitors by chemoinforma-tics and machine learning methods[J].Journal of Medicinal Chemistry,2007,50(19):4665-4668. [10] CHOWDHURY M,ABACHA A B,LAVELLI A,et al.TwoDifferent Machine Learning Techniques for Drug-Drug Interaction Extraction[C]//Proceedings of DDIExtraction2011:First Challenge Task:Drug-Drug Interaction Extraction.2011. [11] LIU S,KAI C,CHEN Q,et al.Dependency-based convolutional neural network for drug-drug interaction extraction[C]//IEEE International Conference on Bioinformatics & Biomedicine.IEEE,2017. [12] RYU J Y,KIM H U,SANG Y L.Deep learning improves prediction of drug-drug and drug-food interactions[J].Proceedings of the National Academy of Sciences of the United States of America,2018,115(18):E4304. [13] HUANG K,XIAO C,HOANG T,et al.Caster:Predicting drug interactions with chemical substructure representation[J].Proceedings of the AAAI Conference on Artificial Intelligence,2020,34(1):702-709. [14] VASWANI A,SHAZEER N,PARMAR N,et al.Attention isall you need[J].Advances in Neural Information Processing Systems,2017,30. [15] RAFFEL C,SHAZEER N,ROBERTS A,et al.Exploring the limits of transfer learning with a unified text-to-text transformer[J].arXiv:1910.10683,2019. [16] GOTTLIEB A,STEIN G Y,ORON Y,et al.INDI:a computational framework for inferring drug interactions and their associated recommendations[J].Molecular Systems Biology,2012,8(1):592. [17] CHENG F,ZHAO Z.Machine learning-based prediction ofdrug-drug interactions by integrating drug phenotypic,therapeutic,chemical,and genomic properties[J].Journal of the American Medical Informatics Association,2014,21(e2):e278-e286. [18] LIM S,LEE K,KANG J.Drug drug interaction extraction from the literature using a recursive neural network[J].PloS One,2018,13(1):e0190926. [19] ZITNIK S M M,SOSIC R,LESKOVEC J.BioSNAP Datasets:Stanford biomedical network dataset collection[OL].http://snap.stanford.edu/biodata. [20] WISHART D S,KNOX C,GUO A C,et al.DrugBank:a know-ledgebase for drugs,drug actions and drug targets[J].Nucleic acids research,2008,36(suppl_1):D901-D906. [21] VILAR S,URIARTE E,SANTANA L,et al.Similarity-based modeling in large-scale prediction of drug-drug interactions[J].Nature Protocols,2014,9(9):2147-2163. [22] JAEGER S,FULLE S,TURK S.Mol2vec:unsupervised ma-chine learning approach with chemical intuition[J].Journal of Chemical Information and Modeling,2018,58(1):27-35. [23] GÓMEZ-BOMBARELLI R,WEI J N,DUVENAUD D,et al.Automatic chemical design using a data-driven continuous representation of molecules[J].ACS Central Science,2018,4(2):268-276. |
[1] | 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习 Temporal Knowledge Graph Representation Learning 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204 |
[2] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
[3] | 汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108 |
[4] | 王剑, 彭雨琦, 赵宇斐, 杨健. 基于深度学习的社交网络舆情信息抽取方法综述 Survey of Social Network Public Opinion Information Extraction Based on Deep Learning 计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099 |
[5] | 王馨彤, 王璇, 孙知信. 基于多尺度记忆残差网络的网络流量异常检测模型 Network Traffic Anomaly Detection Method Based on Multi-scale Memory Residual Network 计算机科学, 2022, 49(8): 314-322. https://doi.org/10.11896/jsjkx.220200011 |
[6] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
[7] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
[8] | 汪鸣, 彭舰, 黄飞虎. 基于多时间尺度时空图网络的交通流量预测模型 Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction 计算机科学, 2022, 49(8): 40-48. https://doi.org/10.11896/jsjkx.220100188 |
[9] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
[10] | 侯钰涛, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木. 中文预训练模型研究进展 Advances in Chinese Pre-training Models 计算机科学, 2022, 49(7): 148-163. https://doi.org/10.11896/jsjkx.211200018 |
[11] | 周慧, 施皓晨, 屠要峰, 黄圣君. 基于主动采样的深度鲁棒神经网络学习 Robust Deep Neural Network Learning Based on Active Sampling 计算机科学, 2022, 49(7): 164-169. https://doi.org/10.11896/jsjkx.210600044 |
[12] | 苏丹宁, 曹桂涛, 王燕楠, 王宏, 任赫. 小样本雷达辐射源识别的深度学习方法综述 Survey of Deep Learning for Radar Emitter Identification Based on Small Sample 计算机科学, 2022, 49(7): 226-235. https://doi.org/10.11896/jsjkx.210600138 |
[13] | 赵冬梅, 吴亚星, 张红斌. 基于IPSO-BiLSTM的网络安全态势预测 Network Security Situation Prediction Based on IPSO-BiLSTM 计算机科学, 2022, 49(7): 357-362. https://doi.org/10.11896/jsjkx.210900103 |
[14] | 胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092 |
[15] | 程成, 降爱莲. 基于多路径特征提取的实时语义分割方法 Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction 计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157 |
|