"Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models

Schwaller, Philippe; Gaudin, Théophile; Lányi, Dávid; Bekas, Costas; Laino, Teodoro

doi:10.48550/arxiv.1711.04810

Cited by 5 publications

(9 citation statements)

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“…In the broader context of Importantly, properties such as atomic number (Z) are often encoded using one hot vectors, which are binary, but for spatial efficiency, the integer is used in its place. The RNN model shows a simplified "many to many" recurrent network, with the text above and below the dashed lines indicating a stylized reaction prediction system inspired by the work of Schwaller et al 61 This system takes in reagent and agent SMILES and predicts the variable length product string; however, the LSTM architecture they used is significantly more complex than the one shown above. artificial intelligence, this means that these systems are weak AI, capable of solving only a single, extremely narrow task and not capable of meaningfully answering even slight deviations from the question it was trained on.…”

Section: ■ the Big Picturementioning

confidence: 99%

Deep Learning in Chemistry

Mater

Coote

2019

J. Chem. Inf. Model.

475

326

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Machine learning enables computers to address problems by learning from data. Deep learning is a type of machine learning that uses hierarchical recombination of features to extract pertinent information, and then learn the patterns represented in the data. Over the last eight years, its abilities have increasingly been applied to a wide variety of chemical challenges, from improving computational chemistry, to drug and materials design, and even synthesis planning. This review aims to explain the concepts of deep learning to chemists from any background and will follow this with an overview of the diverse applications demonstrated in the literature. We hope that this will empower the broader chemical community to engage with this burgeoning field and foster the growing movement of deep learning accelerated chemistry.

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Section: ■ the Big Picturementioning

confidence: 99%

Deep Learning in Chemistry

Mater

Coote

2019

J. Chem. Inf. Model.

475

326

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“…Then a molecules, such as Benzene, is represented in SMILES notation as c1ccccc1. It has already been shown that SMILES representation of molecules has been effective in chemoinformatics [2,10,31,32]. This has strengthened our belief that recent advances in the field of deep computational linguistics and generative models might have an immense impact on prototype based drug development.…”

Section: Molecule Representationmentioning

confidence: 99%

“…Molecular raw data can be represented in few ways, and processed with different deep architectures. Among those we can find 2D/3D images served as input to a convolutional neural network (CNN) [9,35], molecular graph representation paired with neural graph embedding methods [5,39], and SMILES stringsmodeled as a language model with recurrent neural network (RNN) [2,10,31] [18] generative model, to learn dense molecular embedding space. At test time, the model is able to generate new molecules from samples of the prior distribution enforced on the latent representation during training.…”

Section: Related Workmentioning

confidence: 99%

Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks

Harel

Radinsky

2018

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Designing a new drug is a lengthy and expensive process. As the space of potential molecules is very large (10 23 − 10 60 ), a common technique during drug discovery is to start from a molecule which already has some of the desired properties. An interdisciplinary team of scientists generates hypothesis about the required changes to the prototype. In this work, we develop an algorithmic unsupervised-approach that automatically generates potential drug molecules given a prototype drug. We show that the molecules generated by the system are valid molecules and significantly different from the prototype drug. Out of the compounds generated by the system, we identified 35 FDA-approved drugs. As an example, our system generated Isoniazid -one of the main drugs for Tuberculosis. The system is currently being deployed for use in collaboration with pharmaceutical companies to further analyze the additional generated molecules.

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“…With a similar goal, Jacob and Lapkin build a stochastic block model (SBM) for the classification of reactions into true or false using reactions in Reaxys (true) and ones randomly generated from known chemicals (false) [199]. Other machine learning-based methods include ones that rank enumerated mechanistic [200][201][202] or pseudo-mechanistic [203] steps, score/rank reaction templates [153,204], score/rank candidate products generated from reaction templates [205], propose reaction products as resulting from sets of graph edits [206,207], and translate reactant SMILES strings to product SMILES strings using models built for natural language processing tasks [208][209][210]. These all formulate reaction prediction differently; for example, the model in ref.…”

Section: Discovering Models Of Chemical Reactivitymentioning

confidence: 99%

Autonomous discovery in the chemical sciences part I: Progress

Coley,

Eyke,

Jensen

2020

Preprint

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This two-part review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this first part, we describe a classification for discoveries of physical matter (molecules, materials, devices), processes, and models and how they are unified as search problems. We then introduce a set of questions and considerations relevant to assessing the extent of autonomy. Finally, we describe many case studies of discoveries accelerated by or resulting from computer assistance and automation from the domains of synthetic chemistry, drug discovery, inorganic chemistry, and materials science. These illustrate how rapid advancements in hardware automation and machine learning continue to transform the nature of experimentation and modelling.Part two reflects on these case studies and identifies a set of open challenges for the field.

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"Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models

Cited by 5 publications

References 0 publications

Deep Learning in Chemistry

Deep Learning in Chemistry

Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks

Autonomous discovery in the chemical sciences part I: Progress

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