Ru Xiaoqing, Ye Xiucai, Sakurai Tetsuya, Zou Quan
Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan.
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324000, China.
Bioinformatics. 2022 Mar 28;38(7):1964-1971. doi: 10.1093/bioinformatics/btac048.
Drug-target interaction prediction plays an important role in new drug discovery and drug repurposing. Binding affinity indicates the strength of drug-target interactions. Predicting drug-target binding affinity is expected to provide promising candidates for biologists, which can effectively reduce the workload of wet laboratory experiments and speed up the entire process of drug research. Given that, numerous new proteins are sequenced and compounds are synthesized, several improved computational methods have been proposed for such predictions, but there are still some challenges. (i) Many methods only discuss and implement one application scenario, they focus on drug repurposing and ignore the discovery of new drugs and targets. (ii) Many methods do not consider the priority order of proteins (or drugs) related to each target drug (or protein). Therefore, it is necessary to develop a comprehensive method that can be used in multiple scenarios and focuses on candidate order.
In this study, we propose a method called NerLTR-DTA that uses the neighbor relationship of similarity and sharing to extract features, and applies a ranking framework with regression attributes to predict affinity values and priority order of query drug (or query target) and its related proteins (or compounds). It is worth noting that using the characteristics of learning to rank to set different queries can smartly realize the multi-scenario application of the method, including the discovery of new drugs and new targets. Experimental results on two commonly used datasets show that NerLTR-DTA outperforms some state-of-the-art competing methods. NerLTR-DTA achieves excellent performance in all application scenarios mentioned in this study, and the rm(test)2 values guarantee such excellent performance is not obtained by chance. Moreover, it can be concluded that NerLTR-DTA can provide accurate ranking lists for the relevant results of most queries through the statistics of the association relationship of each query drug (or query protein). In general, NerLTR-DTA is a powerful tool for predicting drug-target associations and can contribute to new drug discovery and drug repurposing.
The proposed method is implemented in Python and Java. Source codes and datasets are available at https://github.com/RUXIAOQING964914140/NerLTR-DTA.
药物-靶点相互作用预测在新药发现和药物再利用中起着重要作用。结合亲和力表明药物-靶点相互作用的强度。预测药物-靶点结合亲和力有望为生物学家提供有前景的候选物，可有效减少湿实验室实验的工作量并加速药物研究的整个过程。鉴于此，大量新蛋白质被测序且化合物被合成，针对此类预测已提出了几种改进的计算方法，但仍存在一些挑战。（i）许多方法仅讨论并实现一种应用场景，它们专注于药物再利用而忽略了新药和靶点的发现。（ii）许多方法未考虑与每个靶标药物（或蛋白质）相关的蛋白质（或药物）的优先级顺序。因此，有必要开发一种可用于多种场景并关注候选顺序的综合方法。
在本研究中，我们提出了一种名为NerLTR-DTA 的方法，该方法利用相似性和共享的邻域关系来提取特征，并应用具有回归属性的排序框架来预测查询药物（或查询靶点）及其相关蛋白质（或化合物）的亲和力值和优先级顺序。值得注意的是，利用排序学习的特性设置不同的查询可以巧妙地实现该方法的多场景应用，包括新药和新靶点的发现。在两个常用数据集上的实验结果表明，NerLTR-DTA 优于一些最先进的竞争方法。NerLTR-DTA 在本研究提及的所有应用场景中均取得了优异的性能，并且rm(test)2 值保证了这种优异性能并非偶然获得。此外，通过对每个查询药物（或查询蛋白质）的关联关系进行统计可以得出，NerLTR-DTA 可以为大多数查询的相关结果提供准确的排序列表。总体而言，NerLTR-DTA 是预测药物-靶点关联的强大工具，可为新药发现和药物再利用做出贡献。
所提出的方法用 Python 和 Java 实现。源代码和数据集可在 https://github.com/RUXIAOQING964914140/NerLTR-DTA 获得。
