De novo Molecular Design with Generative Long Short-term Memory

Grisoni, Francesca; Schneider, Gisbert

doi:10.2533/chimia.2019.1006

Cited by 16 publications

(15 citation statements)

References 75 publications

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“…In addition, unlike any of the other methods described, methods such as VAEs are generative: moving around in the latent space and applying the vector so created to the decoder allows for the generation of entirely new molecules (e.g., [ 41 , 42 , 43 , 44 , 45 , 48 , 50 , 63 , 65 , 73 , 74 , 133 ]). This opens up a considerable area of chemical exploration, even in the absence of any knowledge of bioactivities.…”

Section: Discussionmentioning

confidence: 99%

“…More recently, it was recognised that various kinds of architectures could, in fact, permit the reversal of this numerical encoding so as to return a molecule (or its SMILES string encoding a unique structure). These are known as generative methods [ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 ], and at heart their aim to generate a suitable and computationally useful representation [ 56 ] of the input data. It is common (but cf.…”

Section: Introductionmentioning

confidence: 99%

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VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder

et al. 2020

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Molecular similarity is an elusive but core “unsupervised” cheminformatics concept, yet different “fingerprint” encodings of molecular structures return very different similarity values, even when using the same similarity metric. Each encoding may be of value when applied to other problems with objective or target functions, implying that a priori none are “better” than the others, nor than encoding-free metrics such as maximum common substructure (MCSS). We here introduce a novel approach to molecular similarity, in the form of a variational autoencoder (VAE). This learns the joint distribution p(z|x) where z is a latent vector and x are the (same) input/output data. It takes the form of a “bowtie”-shaped artificial neural network. In the middle is a “bottleneck layer” or latent vector in which inputs are transformed into, and represented as, a vector of numbers (encoding), with a reverse process (decoding) seeking to return the SMILES string that was the input. We train a VAE on over six million druglike molecules and natural products (including over one million in the final holdout set). The VAE vector distances provide a rapid and novel metric for molecular similarity that is both easily and rapidly calculated. We describe the method and its application to a typical similarity problem in cheminformatics.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder

et al. 2020

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“…When making use of feature attribution approaches, it is advisable to choose comprehensible molecular descriptors or representations for model construction (Box 2). Recently, architectures borrowed from the natural language processing field, such as long short-term memory networks 76 and transformers 77 , have been used as feature attribution techniques to identify portions of simplified molecular input line entry systems (SMILES) 78 strings that are relevant for bioactivity or physicochemical properties 79,80 . These approaches constitute a first attempt to bridge the gap between the deep learning and medicinal chemistry communities, by relying on representations (atom and bond types, and molecular connectivity 78 ) that bear direct chemical meaning and need no posterior descriptor-to-molecule decoding.…”

Section: Relevance Of Input Featuresmentioning

confidence: 99%

Drug discovery with explainable artificial intelligence

2020

Self Cite

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arious concepts of 'artificial intelligence' (AI) have been successfully adopted for computer-assisted drug discovery in the past few years 1-3. This advance is mostly owed to the ability of deep learning algorithms, that is, artificial neural networks with multiple processing layers, to model complex nonlinear inputoutput relationships, and perform pattern recognition and feature extraction from low-level data representations. Certain deep learning models have been shown to match or even exceed the performance of the familiar existing machine learning and quantitative structure-activity relationship (QSAR) methods for drug discovery 4-6. Moreover, deep learning has boosted the potential and broadened the applicability of computer-assisted discovery, for example, in molecular design 7,8 , chemical synthesis planning 9,10 , protein structure prediction 11 and macromolecular target identification 12,13. The ability to capture intricate nonlinear relationships between input data (for example, chemical structure representations) and the associated output (for example, assay readout) often comes at the price of limited comprehensibility of the resulting model. While there have been efforts to explain QSARs in terms of algorithmic insights and molecular descriptor analysis 14-19 , deep neural network models notoriously elude immediate accessibility by the human mind 20. In medicinal chemistry in particular, the availability of 'rules of thumb' correlating biological effects with physicochemical properties underscores the willingness, in certain situations, to sacrifice accuracy in favour of models that better fit human intuition 21-23. Thus, blurring the lines between the 'two QSARs' 24 (that is, mechanistically interpretable versus highly accurate models) may be key to accelerated drug discovery with AI 25. Automated analysis of medical and chemical knowledge to extract and represent features in a human-intelligible format dates back to the 1990s 26,27 , but has been receiving increasing attention due to the re-emergence of neural networks in chemistry and healthcare. Given the current pace of AI in drug discovery and related fields, there will be an increased demand for methods that help us understand and interpret the underlying models. In an effort to mitigate the lack of interpretability of certain machine learning models, and to augment human reasoning and decision-making, 28 , attention has been drawn to explainable AI (XAI) approaches 29,30. Providing informative explanations alongside the mathematical models aims to (1) render the underlying decision-making process transparent ('understandable') 31 , (2) avoid correct predictions for the wrong reasons (the so-called clever Hans effect) 32 , (3) avert unfair biases or unethical discrimination 33 and (4) bridge the gap between the machine learning community and other scientific disciplines. In addition, effective XAI can help scientists navigate 'cognitive valleys' 28 , allowing them to hone their knowledge and beliefs on the investigated process 34. While the e...

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“…Where the next character (word) is depends on the previous character (word) in a particular word (sentence); in a molecular generation task, the next SMILES character depends in part on the previous character. So For further details, see text and [83] and [84]. (D) Autoencoder net.…”

Section: Recurrent Neural Nets (Rnns)mentioning

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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De novo Molecular Design with Generative Long Short-term Memory

Cited by 16 publications

References 75 publications

VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder

VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder

Drug discovery with explainable artificial intelligence

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

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