Open-source chemogenomic data-driven algorithms for predicting drug-target interactions
Open-source chemogenomic data-driven algorithms for predicting drug-target interactions
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DOI：
10.1093/bib/bby010
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发表时间：
2019-07-01
影响因子：
9.5
通讯作者：
Wang, Yanli
中科院分区：
文献类型：
--
作者：
Hao, Ming;Bryant, Stephen H.;Wang, Yanli
虽然在过去的几十年里，随着制药公司的大量投资，高通量筛选等新技术得到了发展，但药物开发的成功率尚未提高，这促使研究人员寻找新的药物发现策略。药物重新定位是解决这一困境的一个潜在途径。然而，对人类基因组编码的潜在药物靶点进行实验鉴定和验证既昂贵又耗时。因此，人们提出了有效的计算方法来促进药物重新定位，这在药物发现中已被证明是成功的。毫无疑问，来自基础化学生物学研究的开放数据的可用性和人类基因组测序的成功对于开发有效的计算机药物重新定位方法至关重要，这种方法可以识别现有药物的潜在靶点。在这项工作中，我们回顾了几种化学基因组数据驱动的计算算法，其源代码可公开访问，用于预测药物-靶标相互作用（DTIs）。我们根据模型属性和模型演化关系来组织这些算法。我们在R语言中重新实现了5种代表性算法，并通过平均百分位排名（一种新的基于召回率的评价指标）对这些算法进行了比较。我们希望这篇综述对那些希望进一步改进现有算法或需要选择适当算法来推断项目中潜在dti的研究人员来说是客观的和有帮助的。DTI预测的源代码可在：https://github.com/ minghao2016/chemogenomicAlg4DTIpred。
While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/ minghao2016/chemogenomicAlg4DTIpred.