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Review
. 2018 Mar 30;20(3):58.
doi: 10.1208/s12248-018-0210-0.

Deep Learning for Drug Design: An Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era

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Free PMC article
Review

Deep Learning for Drug Design: An Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era

Yankang Jing et al. AAPS J. .
Free PMC article

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Abstract

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.

Keywords: artificial intelligence; artificial neural networks; big data; deep learning; drug discovery.

Figures

Fig. 1
Fig. 1
Architecture of artificial neural networks
Fig. 2
Fig. 2
Comparison of number of publications using different machine learning methods in small molecule drug discovery in recent one decade (SVM: support vector machine, ANN: artificial neural network, Bayesian: Bayesian methods including naive Bayes classifier and Bayesian network)
Fig. 3
Fig. 3
The explosive growth of published bioactivity data (a) and chemicals (b) for small molecule drug discovery in ChEMBL database (based on ChEMBL database releases from 2010 to 2017)
Fig. 4
Fig. 4
a Structure of convolutional layer. b Architecture of convolutional neural network (LeNet-5)
Fig. 5
Fig. 5
Framework of basic recurrent neural network. Recurrent neural network consists of input units (x, the vector representing the matrix of input data) and hidden units (s, the vector representing the matrix in the hidden layer), and output units (o, the vector representing the matrix of output data). U, V, and W are the weight matrixes for the transition from x to s, s to s, and s to o, respectively

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