Recent advances in artificial intelligence for retrosynthesis
- URL: http://arxiv.org/abs/2301.05864v1
- Date: Sat, 14 Jan 2023 09:29:39 GMT
- Title: Recent advances in artificial intelligence for retrosynthesis
- Authors: Zipeng Zhong, Jie Song, Zunlei Feng, Tiantao Liu, Lingxiang Jia,
Shaolun Yao, Tingjun Hou, Mingli Song
- Abstract summary: 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.
- Score: 29.32667622776065
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Retrosynthesis is the cornerstone of organic chemistry, providing chemists in
material and drug manufacturing access to poorly available and brand-new
molecules. Conventional rule-based or expert-based computer-aided synthesis has
obvious limitations, such as high labor costs and limited search space. In
recent years, dramatic breakthroughs driven by artificial intelligence have
revolutionized retrosynthesis. Here we aim to present a comprehensive review of
recent advances in AI-based retrosynthesis. For single-step and multi-step
retrosynthesis both, we first list their goal and provide a thorough taxonomy
of existing methods. Afterwards, we analyze these methods in terms of their
mechanism and performance, and introduce popular evaluation metrics for them,
in which we also provide a detailed comparison among representative methods on
several public datasets. In the next part we introduce popular databases and
established platforms for retrosynthesis. Finally, this review concludes with a
discussion about promising research directions in this field.
Related papers
- SDDBench: A Benchmark for Synthesizable Drug Design [31.739548311094843]
We propose a new, data-driven metric to evaluate molecule synthesizability.
Our approach directly assesses the feasibility of synthetic routes for a given molecule through our proposed round-trip score.
To demonstrate the efficacy of our method, we conduct a comprehensive evaluation of round-trip scores alongside search success rate across a range of representative molecule generative models.
arXiv Detail & Related papers (2024-11-13T03:08:33Z) - 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) - 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) - An Autonomous Large Language Model Agent for Chemical Literature Data
Mining [60.85177362167166]
We introduce an end-to-end AI agent framework capable of high-fidelity extraction from extensive chemical literature.
Our framework's efficacy is evaluated using accuracy, recall, and F1 score of reaction condition data.
arXiv Detail & Related papers (2024-02-20T13:21:46Z) - MechRetro is a chemical-mechanism-driven graph learning framework for
interpretable retrosynthesis prediction and pathway planning [10.364476820771607]
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.
arXiv Detail & Related papers (2022-10-06T01:27:53Z) - 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) - 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 To Navigate The Synthetically Accessible Chemical Space Using
Reinforcement Learning [75.95376096628135]
We propose a novel forward synthesis framework powered by reinforcement learning (RL) for de novo drug design.
In this setup, the agent learns to navigate through the immense synthetically accessible chemical space.
We describe how the end-to-end training in this study represents an important paradigm in radically expanding the synthesizable chemical space.
arXiv Detail & Related papers (2020-04-26T21:40:03Z)
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.