Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review.
Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review.
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DOI：
10.2174/1381612824666180607124038
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发表时间：
2018
影响因子：
3.1
通讯作者：
Huang X
中科院分区：
文献类型：
--
作者：
Carpenter KA;Huang X
虚拟筛选（VS）已成为药物开发过程中的重要工具，因为它对数百万种化合物进行了有效的计算机搜索，最终提高了潜在药物的产量。作为人工智能（AI）的一个子集，机器学习（ML）是对药物线索进行VS的强大方式。VS的ML通常涉及组装一组过滤的化合物训练集，包括已知的活性成分和非活性成分。在训练模型之后，它被验证，并且如果足够准确，则用于以前看不见的数据库以筛选具有所需药物靶点结合活性的新型化合物。 该研究旨在回顾用于VS的基于ML的方法以及在阿尔茨海默病（AD）药物发现中的应用。 为了更新当前关于VS的ML知识，我们回顾了以下ML技术的背景，解释和VS应用：朴素贝叶斯（NB），k最近邻（kNN），支持向量机（SVM），随机森林（RF）和人工神经网络（ANN）。 所有技术都在VS中取得了成功，但VS的未来可能更倾向于使用神经网络-更具体地说，卷积神经网络（CNN），它是利用卷积的ANN的子集。我们还概念化了一个工作流程，用于引导基于ML的VS用于AD的潜在治疗，AD是一种复杂的神经退行性疾病，没有已知的治愈和预防方法。这既是如何应用审查中早些时候介绍的概念的一个例子，也是今后实施的一个可能的工作流程。 不同的ML技术是VS的强大工具，它们有优点和缺点。基于ML的VS可以应用于AD药物开发。
Virtual Screening (VS) has emerged as an important tool in the drug development process, as it conducts efficient in silico searches over millions of compounds, ultimately increas-ing yields of potential drug leads. As a subset of Artificial Intelligence (AI), Machine Learning (ML) is a powerful way of conducting VS for drug leads. ML for VS generally involves assembling a filtered train-ing set of compounds, comprised of known actives and inactives. After training the model, it is validated and, if sufficiently accurate, used on previously unseen databases to screen for novel compounds with desired drug target binding activity. The study aims to review ML-based methods used for VS and applications to Alzheimer’s Disease (AD) drug discovery. To update the current knowledge on ML for VS, we review thorough backgrounds, explana-tions, and VS applications of the following ML techniques: Naïve Bayes (NB), k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN). All techniques have found success in VS, but the future of VS is likely to lean more largely toward the use of neural networks – and more specifically, Convolutional Neural Networks (CNN), which are a subset of ANN that utilize convolution. We additionally conceptualize a work flow for con-ducting ML-based VS for potential therapeutics for AD, a complex neurodegenerative disease with no known cure and prevention. This both serves as an example of how to apply the concepts introduced earlier in the review and as a potential workflow for future implementation. Different ML techniques are powerful tools for VS, and they have advantages and disad-vantages albeit. ML-based VS can be applied to AD drug development.