METAMDB: METABOLIC ATOM MAPPING DATABASE

MetAMDB (https://metamdb.tu-bs.de/) is an open source metabolic atom mapping database, providing atom mappings for around 75000 metabolic reactions. Each atom mapping can be inspected and downloaded either as a RXN file or as a graphic in SVG format. In addition, MetAMDB offers the possibility of automatically creating atom mapping models based on user-specified metabolic networks. These models can be of any size (small to genome scale) and can subsequently be used in standard 13C metabolic flux analysis software.

Extraction of organic chemistry grammar from unsupervised learning of chemical reactions

Humans use different domain languages to represent, explore, and communicate scientific concepts. During the last few hundred years, chemists compiled the language of chemical synthesis inferring a series of “reaction rules” from knowing how atoms rearrange during a chemical transformation, a process called atom-mapping. Atom-mapping is a laborious experimental task and, when tackled with computational methods, requires continuous annotation of chemical reactions and the extension of logically consistent directives. Here, we demonstrate that Transformer Neural Networks learn atom-mapping information between products and reactants without supervision or human labeling. Using the Transformer attention weights, we build a chemically agnostic, attention-guided reaction mapper and extract coherent chemical grammar from unannotated sets of reactions. Our method shows remarkable performance in terms of accuracy and speed, even for strongly imbalanced and chemically complex reactions with nontrivial atom-mapping. It provides the missing link between data-driven and rule-based approaches for numerous chemical reaction tasks.

Transformer: Linking Atom Mapping and Neural Machine Translation

<div><br></div><div><p> Atom mapping reveals the corresponding relationship between reactant and product atoms in chemical reactions, which is important for drug design, exploration for underlying chemical mechanism, reaction classification and so on. Here, we present a new method that links atom mapping and neural machine translation using the transformer model. In contrast to the previous algorithms, our method runs reaction prediction and captures the information of corresponding atoms in parallel. Meanwhile, we use a set of approximately 360K reactions without atom mapping information for obtaining general chemical knowledge and transfer it to atom mapping task on another dataset which contains 50K atom-mapped reactions. With manual evaluation, the top-1 accuracy of the transformer model in atom mapping reaches 91.4%. we hope our work can provide an important step toward solving the challenge problem of atom mapping in a linguistic perspective.</p></div>

Transformer: Linking Atom Mapping and Neural Machine Translation

<div><br></div><div><p> Atom mapping reveals the corresponding relationship between reactant and product atoms in chemical reactions, which is important for drug design, exploration for underlying chemical mechanism, reaction classification and so on. Here, we present a new method that links atom mapping and neural machine translation using the transformer model. In contrast to the previous algorithms, our method runs reaction prediction and captures the information of corresponding atoms in parallel. Meanwhile, we use a set of approximately 360K reactions without atom mapping information for obtaining general chemical knowledge and transfer it to atom mapping task on another dataset which contains 50K atom-mapped reactions. With manual evaluation, the top-1 accuracy of the transformer model in atom mapping reaches 91.4%. we hope our work can provide an important step toward solving the challenge problem of atom mapping in a linguistic perspective.</p></div>

Atom-to-Atom Mapping: A Benchmarking Study of Popular Mapping Algorithms and Consensus Strategies

Here, we discuss a reaction standardization protocol followed by a comparison of popular Atom-to-atom mapping (AAM) tools (ChemAxon, Indigo, RDTool, NextMove and RXNMapper) as well as some consensus AAM strategies. For this purpose, a dataset of 1851 manually curated and mapped reactions was prepared (the Golden dataset) and used as a reference set. It has been found that RXNMapper possesses the highest accuracy, despite the fact that it has some clear disadvantages. Finally, RXNMapper was selected as the best tool, and it was applied to map the USPTO dataset. The standardization protocol used to prepare the data, as well as the data itself are available in the GitHub repository https://github.com/Laboratoire-de-Chemoinformatique.<br><br><br><br>

Atom-to-Atom Mapping: A Benchmarking Study of Popular Mapping Algorithms and Consensus Strategies

Here, we discuss a reaction standardization protocol followed by a comparison of popular Atom-to-atom mapping (AAM) tools (ChemAxon, Indigo, RDTool, NextMove and RXNMapper) as well as some consensus AAM strategies. For this purpose, a dataset of 1851 manually curated and mapped reactions was prepared (the Golden dataset) and used as a reference set. It has been found that RXNMapper possesses the highest accuracy, despite the fact that it has some clear disadvantages. Finally, RXNMapper was selected as the best tool, and it was applied to map the USPTO dataset. The standardization protocol used to prepare the data, as well as the data itself are available in the GitHub repository https://github.com/Laboratoire-de-Chemoinformatique.<br><br><br><br>

DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions

<p>Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP). We formulate a reaction prediction problem in terms of node-classification in a disconnected graph of source molecules and generalize a graph convolution neural network for disconnected graphs. Here we demonstrate that our approach can successfully predict reaction outcome and atom-mapping during a chemical transformation. A set of experiments using the USPTO dataset demonstrates excellent performance and interpretability of the proposed model. Implicitly learned latent vector representation of chemical reactions strongly correlates with the class of the chemical reaction. Reactions with similar templates group together in the latent vector space.</p>

DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions

<p>Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP). We formulate a reaction prediction problem in terms of node-classification in a disconnected graph of source molecules and generalize a graph convolution neural network for disconnected graphs. Here we demonstrate that our approach can successfully predict reaction outcome and atom-mapping during a chemical transformation. A set of experiments using the USPTO dataset demonstrates excellent performance and interpretability of the proposed model. Implicitly learned latent vector representation of chemical reactions strongly correlates with the class of the chemical reaction. Reactions with similar templates group together in the latent vector space.</p>

DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions

Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP). We formulate a reaction prediction problem in terms of node-classification in a disconnected graph of source molecules and generalize a graph convolution neural network for disconnected graphs. Here we demonstrate that our approach can successfully predict reaction outcome and atom-mapping during a chemical transformation. A set of experiments using the USPTO dataset demonstrates excellent performance and interpretability of the proposed model. Our model uses an unsupervised approach to atom-mapping and bridges the gap between data-driven and traditional rule-based methods. Implicitly learned latent vector representation of chemical reactions strongly correlates with the class of the chemical reaction. Reactions with similar templates group together in the latent vector space.