MechRetro is a chemical-mechanism-driven graph learning framework for interpretable retrosynthesis prediction and pathway planning

• URL: http://arxiv.org/abs/2210.02630v1
• Date: Thu, 6 Oct 2022 01:27:53 GMT
• Title: MechRetro is a chemical-mechanism-driven graph learning framework for interpretable retrosynthesis prediction and pathway planning
• Authors: Yu Wang, Chao Pang, Yuzhe Wang, Yi Jiang, Junru Jin, Sirui Liang, Quan Zou, and Leyi Wei
• Abstract summary: MechRetro is a graph learning framework for interpretable retrosynthetic prediction and pathway planning. By integrating chemical knowledge as prior information, we design a novel Graph Transformer architecture. We demonstrate that MechRetro outperforms the state-of-the-art approaches for retrosynthetic prediction with a large margin on large-scale benchmark datasets.
• Score: 10.364476820771607
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Leveraging artificial intelligence for automatic retrosynthesis speeds up organic pathway planning in digital laboratories. However, existing deep learning approaches are unexplainable, like "black box" with few insights, notably limiting their applications in real retrosynthesis scenarios. Here, we propose MechRetro, a chemical-mechanism-driven graph learning framework for interpretable retrosynthetic prediction and pathway planning, which learns several retrosynthetic actions to simulate a reverse reaction via elaborate self-adaptive joint learning. By integrating chemical knowledge as prior information, we design a novel Graph Transformer architecture to adaptively learn discriminative and chemically meaningful molecule representations, highlighting the strong capacity in molecule feature representation learning. We demonstrate that MechRetro outperforms the state-of-the-art approaches for retrosynthetic prediction with a large margin on large-scale benchmark datasets. Extending MechRetro to the multi-step retrosynthesis analysis, we identify efficient synthetic routes via an interpretable reasoning mechanism, leading to a better understanding in the realm of knowledgeable synthetic chemists. We also showcase that MechRetro discovers a novel pathway for protokylol, along with energy scores for uncertainty assessment, broadening the applicability for practical scenarios. Overall, we expect MechRetro to provide meaningful insights for high-throughput automated organic synthesis in drug discovery.

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)
• BatGPT-Chem: A Foundation Large Model For Retrosynthesis Prediction [65.93303145891628]
  BatGPT-Chem is a large language model with 15 billion parameters, tailored for enhanced retrosynthesis prediction. Our model captures a broad spectrum of chemical knowledge, enabling precise prediction of reaction conditions. This development empowers chemists to adeptly address novel compounds, potentially expediting the innovation cycle in drug manufacturing and materials science.
  arXiv  Detail & Related papers  (2024-08-19T05:17:40Z)
• Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis [57.70772230913099]
  Chemist-X automates the reaction condition recommendation (RCR) task in chemical synthesis with retrieval-augmented generation (RAG) technology. Chemist-X interrogates online molecular databases and distills critical data from the latest literature database. Chemist-X considerably reduces chemists' workload and allows them to focus on more fundamental and creative problems.
  arXiv  Detail & Related papers  (2023-11-16T01:21:33Z)
• Recent advances in artificial intelligence for retrosynthesis [29.32667622776065]
  Retrosynthesis is the cornerstone of organic chemistry, providing chemists in material and drug manufacturing access to poorly available and brand-new molecules. Recent breakthroughs driven by artificial intelligence have revolutionized retrosynthesis.
  arXiv  Detail & Related papers  (2023-01-14T09:29:39Z)
• FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning [58.47265392465442]
  Retrosynthetic planning aims to devise a complete multi-step synthetic route from starting materials to a target molecule. Current strategies use a decoupled approach of single-step retrosynthesis models and search algorithms. We propose a novel framework that utilizes context information for improved retrosynthetic planning.
  arXiv  Detail & Related papers  (2022-09-30T08:44:58Z)
• Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport [49.237577649802034]
  We develop a novel optimal transport-based algorithm termed MROT to enhance their generalization capability for molecular regression problems. MROT significantly outperforms state-of-the-art models, showing promising potential in accelerating the discovery of new substances.
  arXiv  Detail & Related papers  (2022-02-13T04:56:18Z)
• 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)

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.