Retrosynthesis Prediction via Search in (Hyper) Graph
- URL: http://arxiv.org/abs/2402.06772v1
- Date: Fri, 9 Feb 2024 20:25:45 GMT
- Title: Retrosynthesis Prediction via Search in (Hyper) Graph
- Authors: Zixun Lan, Binjie Hong, Jiajun Zhu, Zuo Zeng, Zhenfu Liu, Limin Yu,
Fei Ma
- Abstract summary: Predicting reactants from a specified core product is a fundamental challenge within organic synthesis.
We propose a semi-template-based method, the textbfRetrosynthesis via textbfSearch textbfin (Hyper) textbfGraph (RetroSiG) framework to alleviate these limitations.
- Score: 5.241472125982734
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Predicting reactants from a specified core product stands as a fundamental
challenge within organic synthesis, termed retrosynthesis prediction. Recently,
semi-template-based methods and graph-edits-based methods have achieved good
performance in terms of both interpretability and accuracy. However, due to
their mechanisms these methods cannot predict complex reactions, e.g.,
reactions with multiple reaction center or attaching the same leaving group to
more than one atom. In this study we propose a semi-template-based method, the
\textbf{Retro}synthesis via \textbf{S}earch \textbf{i}n (Hyper) \textbf{G}raph
(RetroSiG) framework to alleviate these limitations. In the proposed method, we
turn the reaction center identification and the leaving group completion tasks
as tasks of searching in the product molecular graph and leaving group
hypergraph respectively. As a semi-template-based method RetroSiG has several
advantages. First, RetroSiG is able to handle the complex reactions mentioned
above by its novel search mechanism. Second, RetroSiG naturally exploits the
hypergraph to model the implicit dependencies between leaving groups. Third,
RetroSiG makes full use of the prior, i.e., one-hop constraint. It reduces the
search space and enhances overall performance. Comprehensive experiments
demonstrated that RetroSiG achieved competitive results. Furthermore, we
conducted experiments to show the capability of RetroSiG in predicting complex
reactions. Ablation experiments verified the efficacy of specific elements,
such as the one-hop constraint and the leaving group hypergraph.
Related papers
- UAlign: Pushing the Limit of Template-free Retrosynthesis Prediction with Unsupervised SMILES Alignment [51.49238426241974]
This paper introduces UAlign, a template-free graph-to-sequence pipeline for retrosynthesis prediction.
By combining graph neural networks and Transformers, our method can more effectively leverage the inherent graph structure of molecules.
arXiv Detail & Related papers (2024-03-25T03:23:03Z) - Retrosynthesis prediction enhanced by in-silico reaction data
augmentation [66.5643280109899]
We present RetroWISE, a framework that employs a base model inferred from real paired data to perform in-silico reaction generation and augmentation.
On three benchmark datasets, RetroWISE achieves the best overall performance against state-of-the-art models.
arXiv Detail & Related papers (2024-01-31T07:40:37Z) - RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation [32.643400484143605]
We introduce Retrosynthesis (RetroDiff), a novel diffusion-based method designed to address this problem.
Our key innovation is to develop a multi-stage diffusion process.
Experimental results on the benchmark have demonstrated the superiority of our method over all other semi-template methods.
arXiv Detail & Related papers (2023-11-23T16:08:52Z) - 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) - 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) - RetCL: A Selection-based Approach for Retrosynthesis via Contrastive
Learning [107.64562550844146]
Retrosynthesis is an emerging research area of deep learning.
We propose a new approach that reformulating retrosynthesis into a selection problem of reactants from a candidate set of commercially available molecules.
For learning the score functions, we also propose a novel contrastive training scheme with hard negative mining.
arXiv Detail & Related papers (2021-05-03T12:47:57Z) - 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) - A Graph to Graphs Framework for Retrosynthesis Prediction [42.99048270311063]
A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule.
We propose a novel template-free approach called G2Gs by transforming a target molecular graph into a set of reactant molecular graphs.
G2Gs significantly outperforms existing template-free approaches by up to 63% in terms of the top-1 accuracy.
arXiv Detail & Related papers (2020-03-28T06:16:56Z)
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.