利用药物靶点网络预测药物的ATC分类法：NetPredATC方法

70 浏览量更新于2024-08-29 收藏 1.67MB PDF 举报

本文主要探讨了网络预测药物的解剖治疗化学(Anatomical Therapeutic Chemical, ATC)编码这一关键问题。随着药物研发的日益复杂，理解药物在分子层面的ATC分类规则对于深入理解药物作用机制具有重要意义。然而，现有的研究在利用计算机方法预测药物潜在的ATC代码方面仍然相对匮乏。研究者们针对这一挑战，提出了一个名为NetPredATC的新方法。他们利用药物-靶点网络作为基础，通过分析药物的化学结构和作用靶点，构建了一个系统的预测框架。NetPredATC的基本假设是，具有相似化学结构或作用于相同靶点的药物，其ATC编码往往具有一定的共性。这种方法旨在通过整合药物的化学信息和生物学特性，智能地推断出药物可能归属的ATC类别。具体操作上，NetPredATC包括以下几个步骤：首先，构建药物与靶点之间的关系网络，这一步骤依赖于大规模的化合物数据库和蛋白质相互作用数据。其次，对网络进行分析，识别潜在的关联模式，这些模式可能是ATC编码的指示信号。然后，结合机器学习算法，如深度神经网络或集成学习方法，对药物的ATC编码进行预测。最后，通过验证和优化模型性能，提高预测的准确性和可靠性。这项研究的意义在于，它不仅能够帮助药理学家快速识别新药的作用领域，还能促进个性化医疗的发展，使得医生可以根据患者的特定疾病和基因特征，更精准地选择合适的药物。此外，NetPredATC的实施也为药物研发提供了新的数据驱动策略，减少了传统实验法在ATC分类预测上的时间和资源消耗。网络预测药物的ATC编码是一项具有前瞻性的研究，它将计算机科学和生物医学领域的知识结合起来，有望在未来推动药物发现和开发的效率，以及提升临床决策的质量。

Network predicting drug’s anatomical therapeutic

chemical code

Yong-Cui Wang

, Shi-Long Chen

, Nai-Yang Deng

and Yong Wang

3 ∗

Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau

Biology, Chinese Academy of Sciences, Xining, China, 810001.

College of Science, China Agricultural University, Beijing, China, 100083.

National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and

Systems Science, Chinese Academy of Sciences, Beijing, China, 100190.

ABSTRACT

Motivation: Discovering drug’s Anatomical Therapeutic Chemical

(ATC) classiﬁcation rules at molecular level is of vital importance to

understand a vast majority of drugs action. However, few studies

attempt to annotate drug’s potential ATC-codes by computational

approaches.

Results: Here, we introduce drug-target network to computationally

predict drug’s ATC-codes and propose a novel method named

NetPredATC. Starting from the assumption that dr ugs with similar

chemical structures or target proteins share common ATC-codes, our

method, NetPredATC, aims to assign drug’s potential ATC-codes by

integrating chemical structures and target proteins. Speciﬁcally, we

ﬁrst construct a gold-standard positive dataset from drugs’ ATC-code

annotation databases. Then we characterize ATC-code and drug by

their similarity proﬁles and deﬁne kernel function to correlate them.

Finally, we utilize a kernel method, support vector machine (SVM),

to automatically predict drug’s ATC-codes. Our method was validated

on four drug datasets with various target proteins, including enzymes,

ion channels (ICs), G-protein couple receptors (GPCRs), and nuclear

receptors (NRs). We found that both drug’s chemical structure

and target protein are predictive and target protein information has

better accuracy. Further integrating these two data sources revealed

more experimentally validated ATC-codes for drugs. We extensively

compared our NetPredATC with SuperPred, which is a chemical

similarity only based method. Experimental results showed that our

NetPredATC outperforms SuperPred not only in predictive coverage

but also in accuracy. In addition, database search and functional

annotation analysis support that our novel predictions are worthy of

future experimental validation.

Conclusion: In conclusion, our new method, NetPredATC, can

predict drug’s ATC-codes more accurately by incorporating drug-

target network and integrating data, which will promote drug

mechanism understanding and drug repositioning and discovery.

Availability: NetPredATC is available at http://doc.aporc.org/

wiki/NetPredATC.

Contact: ycwang@nwipb.cas.cn, ywang@amss.ac.cn

∗

To whom correspondence should be addressed

1 INTRODUCTION

The Anatomical Therapeutic Chemical (ATC) classiﬁcation system

categorizes drug substances at different levels by their therapeutic

properties, chemical properties, pharmacological properties, and

practical applications. This classiﬁcation system is recommended by

the World Health Organization (WHO) and drug’s ATC-codes have

been widely applied in almost all drug utilization studies (WHO,

2006). Speciﬁcally, ATC classiﬁcation system can be used as a basic

tool for drug utilization research. It also provides the presentation

and comparison of drug consumption statistics at international

level. In addition, ATC prediction will greatly facilitate the recent

drug repositioning and drug combination studies. Though useful,

mapping ATC-codes to drugs is quite challenging.

Recently, ATC-codes for some well characterized drugs have

been deposited in databases, such as KEGG BRITE (Kanehisa

et al., 2006) and DrugBank (Wishart et al., 2008). These databases

provide high quality expert curated data. However, they are in

small scale and the coverage is far from enough to serve practical

usage. Even for some well-collected drug datasets, the ATC code

assignments for drugs are far from complete. For example, the

dataset in Yamanishi et al., 2008 contains drugs with four different

type target proteins including enzymes, ion channels (ICs), G-

protein couple receptors (GPCRs), and nuclear receptors (NRs).

These drugs all have manually curated target proteins from KEGG

BRITE (Kanehisa et al., 2006), BRENDA(Schomburg et al., 2004),

SuperTarget (Gunther et al., 2008), and DrugBank (Wishart et al.,

2008). Even in this high-quality dataset, there are 102 drugs which

do not have any ATC-codes in all 445 drugs targeting enzyme, 13

drugs which do not have any ATC-codes in all 210 drugs targeting

IC, 23 drugs which do not have any ATC-codes in all 223 drugs

targeting GPCR, and 4 drugs which do not have any ATC-codes in

all 54 drugs targeting NR. The percent of drugs without ATC codes

varies from 10% to 25%.

The bottleneck is that current data collection procedure heavily

relies on human curation and is not efﬁcient. One way out

is to learn the underlying drug ATC-codes classiﬁcation rules

from the available high quality ATC-code annotations, and

further automatically assign new ATC-codes to drugs by a

computational predictor. This strategy will accelerate the functional

characterization of drugs under the ATC classiﬁcation systems,

Associate Editor: Dr. Olga Troyanskaya

Bioinformatics Advance Access published April 5, 2013

at Periodicals Department/Lane Library on April 5, 2013http://bioinformatics.oxfordjournals.org/Downloaded from

下载后可阅读完整内容，剩余7页未读，立即下载

weixin_38726407

粉丝: 20
资源: 954

利用药物靶点网络预测药物的ATC分类法：NetPredATC方法

2019-MIT-Predicting Drug Responses by Propagating Interactions t

Sample neural networks code predicting stock price in ten days using C++

组学预测法是通过分析药物和靶标的基因表达谱，预测它们之间的相互作用有什么文献吗

the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").中predicting epsilon和predicting x0具体指？

Semi-Supervised Classification with Graph Convolutional Networks

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

Build the LSTM model for predicting a movie review's emotion polarity using the dataset "keras.datasets.imdb".

PYTHON ANN for regression code

v_prob(i,j)=v_main(x,w_s(j,1));

Modify the above code to turn it into a solution to the regression problem

最新资源