Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits

• URL: http://arxiv.org/abs/2006.15426v2
• Date: Tue, 25 May 2021 10:49:41 GMT
• Title: Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits
• Authors: Miko{\l}aj Sacha, Miko{\l}aj B{\l}a\.z, Piotr Byrski, Pawe{\l} D\k{a}browski-Tuma\'nski, Miko{\l}aj Chromi\'nski, Rafa{\l} Loska, Pawe{\l} W{\l}odarczyk-Pruszy\'nski, Stanis{\l}aw Jastrz\k{e}bski
• Abstract summary: We present Molecule Edit Graph Attention Network (MEGAN), an end-to-end encoder-decoder neural model. MEGAN is inspired by models that express a chemical reaction as a sequence of graph edits, akin to the arrow pushing formalism. We extend this model to retrosynthesis prediction (predicting substrates given the product of a chemical reaction) and scale it up to large datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The central challenge in automated synthesis planning is to be able to generate and predict outcomes of a diverse set of chemical reactions. In particular, in many cases, the most likely synthesis pathway cannot be applied due to additional constraints, which requires proposing alternative chemical reactions. With this in mind, we present Molecule Edit Graph Attention Network (MEGAN), an end-to-end encoder-decoder neural model. MEGAN is inspired by models that express a chemical reaction as a sequence of graph edits, akin to the arrow pushing formalism. We extend this model to retrosynthesis prediction (predicting substrates given the product of a chemical reaction) and scale it up to large datasets. We argue that representing the reaction as a sequence of edits enables MEGAN to efficiently explore the space of plausible chemical reactions, maintaining the flexibility of modeling the reaction in an end-to-end fashion, and achieving state-of-the-art accuracy in standard benchmarks. Code and trained models are made available online at https://github.com/molecule-one/megan.

Related papers

• Learning Chemical Reaction Representation with Reactant-Product Alignment [50.28123475356234]
  This paper introduces modelname, a novel chemical reaction representation learning model tailored for a variety of organic-reaction-related tasks. By integrating atomic correspondence between reactants and products, our model discerns the molecular transformations that occur during the reaction, thereby enhancing the comprehension of the reaction mechanism. We have designed an adapter structure to incorporate reaction conditions into the chemical reaction representation, allowing the model to handle diverse reaction conditions and adapt to various datasets and downstream tasks, e.g., reaction performance prediction.
  arXiv  Detail & Related papers  (2024-11-26T17:41:44Z)
• GraphXForm: Graph transformer for computer-aided molecular design with application to extraction [73.1842164721868]
  We present GraphXForm, a decoder-only graph transformer architecture, which is pretrained on existing compounds and then fine-tuned. We evaluate it on two solvent design tasks for liquid-liquid extraction, showing that it outperforms four state-of-the-art molecular design techniques.
  arXiv  Detail & Related papers  (2024-11-03T19:45:15Z)
• Molecule-Edit Templates for Efficient and Accurate Retrosynthesis Prediction [0.16070833439280313]
  We introduce METRO, a machine-learning model that predicts reactions using minimal templates. We achieve state-of-the-art results on standard benchmarks.
  arXiv  Detail & Related papers  (2023-10-11T09:00:02Z)
• MARS: A Motif-based Autoregressive Model for Retrosynthesis Prediction [54.75583184356392]
  We propose a novel end-to-end graph generation model for retrosynthesis prediction. It sequentially identifies the reaction center, generates the synthons, and adds motifs to the synthons to generate reactants. Experiments on a benchmark dataset show that the proposed model significantly outperforms previous state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2022-09-27T06:29:35Z)
• AI-driven Hypergraph Network of Organic Chemistry: Network Statistics and Applications in Reaction Classification [0.0]
  We use a standard reactions dataset to construct a hypernetwork and report its statistics. We also compute each statistic for an equivalent directed graph representation of reactions to draw parallels and highlight differences. We conclude that the hypernetwork representation is flexible, preserves reaction context, and uncovers hidden insights.
  arXiv  Detail & Related papers  (2022-08-02T14:12:03Z)
• Retroformer: Pushing the Limits of Interpretable End-to-end Retrosynthesis Transformer [15.722719721123054]
  Retrosynthesis prediction is one of the fundamental challenges in organic synthesis. We propose Retroformer, a novel Transformer-based architecture for retrosynthesis prediction. Retroformer reaches the new state-of-the-art accuracy for the end-to-end template-free retrosynthesis.
  arXiv  Detail & Related papers  (2022-01-29T02:03:55Z)
• RetroXpert: Decompose Retrosynthesis Prediction like a Chemist [60.463900712314754]
  We devise a novel template-free algorithm for automatic retrosynthetic expansion. Our method disassembles retrosynthesis into two steps. While outperforming the state-of-the-art baselines, our model also provides chemically reasonable interpretation.
  arXiv  Detail & Related papers  (2020-11-04T04:35:34Z)
• Learning Graph Models for Retrosynthesis Prediction [90.15523831087269]
  Retrosynthesis prediction is a fundamental problem in organic synthesis. This paper introduces a graph-based approach that capitalizes on the idea that the graph topology of precursor molecules is largely unaltered during a chemical reaction. Our model achieves a top-1 accuracy of $53.7%$, outperforming previous template-free and semi-template-based methods.
  arXiv  Detail & Related papers  (2020-06-12T09:40:42Z)
• Retrosynthesis Prediction with Conditional Graph Logic Network [118.70437805407728]
  Computer-aided retrosynthesis is finding renewed interest from both chemistry and computer science communities. We propose a new approach to this task using the Conditional Graph Logic Network, a conditional graphical model built upon graph neural networks.
  arXiv  Detail & Related papers  (2020-01-06T05:36:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.