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A new chemoinformatics approach with improved strategies for effective predictions of potential drugs.
Journal of Cheminformatics ( IF 8.6 ) Pub Date : 2018-10-11 , DOI: 10.1186/s13321-018-0303-x
Ming Hao 1 , Stephen H Bryant 1 , Yanli Wang 1
Affiliation  

Fast and accurate identification of potential drug candidates against therapeutic targets (i.e., drug–target interactions, DTIs) is a fundamental step in the early drug discovery process. However, experimental determination of DTIs is time-consuming and costly, especially for testing the associations between the entire chemical and genomic spaces. Therefore, computationally efficient algorithms with accurate predictions are required to achieve such a challenging task. In this work, we design a new chemoinformatics approach derived from neighbor-based collaborative filtering (NBCF) to infer potential drug candidates for targets of interest. One of the fundamental steps of NBCF in the application of DTI predictions is to accurately measure the similarity between drugs solely based on the DTI profiles of known knowledge. However, commonly used similarity calculation methods such as COSINE may be noise-prone due to the extremely sparse property of the DTI bipartite network, which decreases the model performance of NBCF. We herein propose three strategies to remedy such a dilemma, which include: (1) adopting a positive pointwise mutual information (PPMI)-based similarity metric, which is noise-immune to some extent; (2) performing low-rank approximation of the original prediction scores; (3) incorporating auxiliary (complementary) information to produce the final predictions. We test the proposed methods in three benchmark datasets and the results indicate that our strategies are helpful to improve the NBCF performance for DTI predictions. Comparing to the prior algorithm, our methods exhibit better results assessed by a recall-based evaluation metric. A new chemoinformatics approach with improved strategies was successfully developed to predict potential DTIs. Among them, the model based on the sparsity resistant PPMI similarity metric exhibits the best performance, which may be helpful to researchers for identifying potential drugs against therapeutic targets of interest, and can also be applied to related research such as identifying candidate disease genes.

中文翻译:

一种新的化学信息学方法,具有改进的策略,可有效预测潜在药物。

快速,准确地识别针对治疗靶标的潜在候选药物(即药物-靶标相互作用,DTI)是早期药物发现过程中的基本步骤。但是,对DTI进行实验确定是耗时且昂贵的,特别是对于测试整个化学空间和基因组空间之间的关联而言。因此,需要具有精确预测的计算有效算法来完成这一具有挑战性的任务。在这项工作中,我们设计了一种新的化学信息学方法,该方法源自基于邻居的协作过滤(NBCF),以推断潜在的候选药物作为目标靶标。NBCF在DTI预测中的应用的基本步骤之一是仅基于已知知识的DTI概况准确测量药物之间的相似性。然而,由于DTI双向网络的稀疏特性,因此常用的相似度计算方法(例如COSINE)可能易于产生噪声,从而降低了NBCF的模型性能。本文中,我们提出了三种策略来解决这种难题,包括:(1)采用基于正点向互信息(PPMI)的相似性度量,该度量在一定程度上不受噪声影响;(2)对原始预测分数进行低秩逼近;(3)合并辅助(补充)信息以产生最终预测。我们在三个基准数据集中测试了所提出的方法,结果表明我们的策略有助于提高DTI预测的NBCF性能。与现有算法相比,我们的方法通过基于召回的评估指标评估出更好的结果。成功开发了一种具有改进策略的新化学信息学方法来预测潜在的DTI。其中,基于抗稀疏性PPMI相似性度量的模型表现出最好的性能,这可能有助于研究人员识别针对感兴趣的治疗靶标的潜在药物,还可以应用于相关研究,例如识别候选疾病基因。
更新日期:2018-10-11
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