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بخشی از مقاله انگلیسیعنوان انگلیسی:The rise of deep learning in drug discovery~~en~~

Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine learning algorithms in areas such as image and voice recognition, natural language processing, among others. The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples will be discussed covering bioactivity prediction, de novo molecular design, synthesis prediction and biological image analysis.

Introduction

Digital data, in all shapes and sizes, is growing exponentially. According to the National Security Agency of the USA, the Internet is processing 1826 petabytes of data per day [1]. In 2011, digital information grew nine times in volume in just five years [2]; and by 2020 its amount in the world is expected to reach 35 trillion gigabytes [3]. The high demand of exploring and analyzing big data has encouraged the use of data-hungry machine learning algorithms like deep learning (DL). DL has gained huge success in a wide range of applications such as computer games, speech recognition, computer vision, natural language processing, selfdriving cars, among others [4]. It is fair to say that DL is changing our everyday life. In the Gartner-selected top ten technology trends of 2018, DL-represented AI technologies were ranked at the top position [5]. Over the past decade, there has been a remarkable increase in the amount of available compound activity and biomedical data [6,7] owing to the emergence of new experimental techniques such as HTS, parallel synthesis, among others [7,8]. How to efficiently mine the large-scale chemistry data becomes a crucial problem for drug discovery. Larger data volumes in combination with increased automation technology have promoted further use of machine learning. Besides established methods like support vector machines (SVM) [9], neural networks (NN) [10] and random forest (RF) [11], which have been utilized to develop QSAR models for a long time, methods like matrix factorization [12] and DL have started to be used. DL has taken advantage of the increased amounts of data and the continuous increase of available computer power. A difference between most other machine learning methods and DL is the flexibility of the NN architecture in DL. Architectures that will be discussed in this review are convolutional neural networks (CNNs), recurrent neural networks (RNNs) and fully connected feed-forward networks. Single-layer NNs have been used in QSAR modeling for a long time [10]; and with increasing data size and computational power have made it natural to apply multilayer feed-forward networks for bioactivity predictions. A somewhat surprising development has been the use of RNNs in de novo design which could not be foreseen a few years ago. With the adoption of high-throughput imaging equipment, CNNs have gained remarkable success in computer vision and have become a natural choice for biological image processing. The field of applying DL in drug discovery is rapidly progressing with new articles published almost every week. Recently, several reviews on DL applications in computational chemistry and life sciences have been published [13–]. Here, we focus more on DL applications in drug discovery particularly in the chemoinformatics and biological image analysis domains and highlight DL architectures used so far within drug discovery.

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