Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.
Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.
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DOI：
10.1208/s12248-018-0210-0
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发表时间：
2018-03-30
期刊：
影响因子：
--
通讯作者：
Xie XQ
中科院分区：
文献类型：
--
作者：
Jing Y;Bian Y;Hu Z;Wang L;Xie XQ
在过去的十年中，深度学习（DL）方法已经非常成功，并被广泛用于开发几乎每个领域的人工智能（AI），特别是在它在计算围棋上取得了令人自豪的记录之后。与传统的机器学习算法相比，深度学习方法在小分子药物发现和开发中实现识别还有很长的路要走。但是，为了推广和应用研究型数字图书馆，还有许多工作要做，如：用于小分子药物的研究和开发。在这篇综述中，我们主要讨论了几个最强大和主流的架构，包括卷积神经网络（CNN），递归神经网络（RNN）和深度自动编码器网络（DAEN），用于监督学习和非监督学习;总结了小分子药物设计中的大多数代表性应用;并简要介绍了DL方法如何在这些应用中使用。讨论了深度学习方法的优缺点以及我们需要解决的主要挑战。
Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.
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