揭示药物-靶标相互作用的计算模型和算法。
Revealing Drug-Target Interactions with Computational Models and Algorithms.
机构信息
School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
School of Computer Science, Hunan Institute of Technology, Henyang 421002, Hunan, China.
出版信息
Molecules. 2019 May 2;24(9):1714. doi: 10.3390/molecules24091714.
BACKGROUND
Identifying possible drug-target interactions (DTIs) has become an important task in drug research and development. Although high-throughput screening is becoming available, experimental methods narrow down the validation space because of extremely high cost, low success rate, and time consumption. Therefore, various computational models have been exploited to infer DTI candidates.
METHODS
We introduced relevant databases and packages, mainly provided a comprehensive review of computational models for DTI identification, including network-based algorithms and machine learning-based methods. Specially, machine learning-based methods mainly include bipartite local model, matrix factorization, regularized least squares, and deep learning.
RESULTS
Although computational methods have obtained significant improvement in the process of DTI prediction, these models have their limitations. We discussed potential avenues for boosting DTI prediction accuracy as well as further directions.
背景
识别潜在的药物-靶点相互作用（DTI）已成为药物研发中的一项重要任务。虽然高通量筛选变得越来越可行，但由于成本极高、成功率低和耗时等原因，实验方法缩小了验证空间。因此，各种计算模型已被用于推断 DTI 候选物。
方法
我们介绍了相关数据库和包，主要全面回顾了用于 DTI 识别的计算模型，包括基于网络的算法和基于机器学习的方法。具体来说，基于机器学习的方法主要包括二分局部模型、矩阵分解、正则化最小二乘法和深度学习。
结果
尽管计算方法在 DTI 预测过程中取得了显著的进展，但这些模型仍存在局限性。我们讨论了提高 DTI 预测准确性的潜在途径以及进一步的方向。
Zotero 插件