Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis

Button, Alexander L.; Merk, Daniel; Hiss, Jan A.; Schneider, Gisbert

doi:10.1038/s42256-019-0067-7

Cited by 67 publications

(55 citation statements)

References 24 publications

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“…What we would really like to have is the ability to generate the molecules themselves 'de novo' (e.g. [59,64,65,84,[92][93][94][95][96][97][98][99][100][101][102][103][104][105][106][107]), by learning what amounts to a joint distribution over all the variables (both inputs and outputs). To this end, a generative model seeks to simulate or recreate how the data are generated 'in the real world'.…”

Section: Variational Autoencoders (Vaes) and Generative Methodsmentioning

confidence: 99%

Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently

Kell

Samanta

Swainston

2020

Biochemical Journal

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The number of ‘small’ molecules that may be of interest to chemical biologists — chemical space — is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved ‘forward’ problems (have molecule, establish properties). However, we normally wish to solve the much harder generative or inverse problem (describe desired properties, find molecule). ‘Deep’ (machine) learning based on large-scale neural networks underpins technologies such as computer vision, natural language processing, driverless cars, and world-leading performance in games such as Go; it can also be applied to the solution of inverse problems in chemical biology. In particular, recent developments in deep learning admit the in silico generation of candidate molecular structures and the prediction of their properties, thereby allowing one to navigate (bio)chemical space intelligently. These methods are revolutionary but require an understanding of both (bio)chemistry and computer science to be exploited to best advantage. We give a high-level (non-mathematical) background to the deep learning revolution, and set out the crucial issue for chemical biology and informatics as a two-way mapping from the discrete nature of individual molecules to the continuous but high-dimensional latent representation that may best reflect chemical space. A variety of architectures can do this; we focus on a particular type known as variational autoencoders. We then provide some examples of recent successes of these kinds of approach, and a look towards the future.

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Section: Variational Autoencoders (Vaes) and Generative Methodsmentioning

confidence: 99%

Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently

Kell

Samanta

Swainston

2020

Biochemical Journal

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“…[4][5][6] In the context of in silico drug discovery, various techniques, such as virtual screening and de novo drug design, have been proposed to discover novel molecular structures in the vast chemical space. 7,8) Early works of de novo drug design used various algorithms, such as atom based elongation, fragment based combination, and reaction-driven design, to generate new molecular structures. [9][10][11][12][13] The structure-activity relationship (SAR) Matrix (SARM) method was designed to combine SAR visualization with new compound design.…”

Section: Introductionmentioning

confidence: 99%

Strategies for Design of Molecular Structures with a Desired Pharmacophore Using Deep Reinforcement Learning

et al. 2020

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The goal of drug design is to discover molecular structures that have suitable pharmacological properties in vast chemical space. In recent years, the use of deep generative models (DGMs) is getting a lot of attention as an effective method of generating new molecules with desired properties. However, most of the properties do not have three-dimensional (3D) information, such as shape and pharmacophore. In drug discovery, pharmacophores are valuable clues in finding active compounds. In this study, we propose a computational strategy based on deep reinforcement learning for generating molecular structures with a desired pharmacophore. In addition, to extract selective molecules against a target protein, chemical genomics-based virtual screening (CGBVS) is used as post-processing method of deep reinforcement learning. As an example study, we have employed this strategy to generate molecular structures of selective TIE2 inhibitors. This strategy can be adopted into general use for generating selective molecules with a desired pharmacophore.

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“…Recently, chemists enthusiastically applied advanced machine learning and artificial intelligence (AI) technologies toward the synthesis of drug molecules. For example, the AI-driven discovery of drug molecules [4][5][6], automated planning of synthetic routes [7][8][9], machine learning-driven optimization of reaction conditions [10][11][12] and autonomous assembly of synthetic processes [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Hard-threshold neural network-based prediction of organic synthetic outcomes

Yuan

2020

BMC Chem Eng

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Retrosynthetic analysis is a canonical technique for planning the synthesis route of organic molecules in drug discovery and development. In this technique, the screening of synthetic tree branches requires accurate forward reaction prediction, but existing software is far from completing this step independently. Previous studies attempted to apply a neural network to forward reaction prediction, but the accuracy was not satisfying. Through using the Edit Vector-based description and extended-connectivity fingerprints to transform the reaction into a vector, this study focuses on the update of the neural network to improve the template-based forward reaction prediction. Hard-threshold activation and the target propagation algorithm are implemented by introducing mixed convex-combinatorial optimization. Comparative tests were conducted to explore the optimal hyperparameter set. Using 15,000 experimental reaction data extracted from granted United States patents, the proposed hard-threshold neural network was systematically trained and tested. The results demonstrated that a higher prediction accuracy was obtained than that for the traditional neural network with backpropagation algorithm. Some successfully predicted reaction examples are also briefly illustrated.

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Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis

Cited by 67 publications

References 24 publications

Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently

Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently

Strategies for Design of Molecular Structures with a Desired Pharmacophore Using Deep Reinforcement Learning

Hard-threshold neural network-based prediction of organic synthetic outcomes

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