Template-free retrosynthesis methods treat the task as black-box sequence generation, limiting learning efficiency, while semi-template approaches rely on rigid reaction libraries that constrain generalization. We address this gap with a key insight: atom ordering in neural representations matters. Building on this insight, we propose a structure-aware template-free framework that encodes the two-stage nature of chemical reactions as a positional inductive bias. By placing reaction center atoms at the sequence head, our method transforms implicit chemical knowledge into explicit positional patterns that the model can readily capture. The proposed RetroDiT backbone, a graph transformer with rotary position embeddings, exploits this ordering to prioritize chemically critical regions. Combined with discrete flow matching, our approach decouples training from sampling and enables generation in 20--50 steps versus 500 for prior diffusion methods. Our method achieves state-of-the-art performance on both USPTO-50k (61.2% top-1) and the large-scale USPTO-Full (51.3% top-1) with predicted reaction centers. With oracle centers, performance reaches 71.1% and 63.4% respectively, surpassing foundation models trained on 10 billion reactions while using orders of magnitude less data. Ablation studies further reveal that structural priors outperform brute-force scaling: a 280K-parameter model with proper ordering matches a 65M-parameter model without it.
University of Science and Technology of China（中国科学技术大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）
Single-step retrosynthesis is the cornerstone of retrosynthesis planning, which is a crucial task for computer-aided drug discovery. The goal of single-step retrosynthesis is to identify the possible reactants that lead to the synthesis of the target product in one reaction. By representing organic molecules as canonical strings, existing sequence-based retrosynthetic methods treat the product-to-reactant retrosynthesis as a sequence-to-sequence translation problem. However, most of them struggle to identify diverse chemical reactions for a desired product due to the deterministic inference, w…
RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation
Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；ByteDance Research（字节跳动研究院）；Department of Computer Science, Stanford University（斯坦福大学计算机科学系）
Google Cloud AI（谷歌云人工智能）；Google Brain（谷歌大脑）；Google Research（谷歌研究院）
Retrosynthesis -- the process of identifying a set of reactants to synthesize a target molecule -- is of vital importance to material design and drug discovery. Existing machine learning approaches based on language models and graph neural networks have achieved encouraging results. In this paper, we propose a framework that unifies sequence- and graph-based methods as energy-based models (EBMs) with different energy functions. This unified perspective provides critical insights about EBM variants through a comprehensive assessment of performance. Additionally, we present a novel dual variant …
Synthesis planning and reaction outcome prediction are two fundamental problems in computer-aided organic chemistry for which a variety of data-driven approaches have emerged. Natural language approaches that model each problem as a SMILES-to-SMILES translation lead to a simple end-to-end formulation, reduce the need for data preprocessing, and enable the use of well-optimized machine translation model architectures. However, SMILES representations are not an efficient representation for capturing information about molecular structures, as evidenced by the success of SMILES augmentation to boo…
Enhancing Chemical Reaction and Retrosynthesis Prediction with Large Language Model and Dual-task Learning
School of Computer Science, Xiangtan University（湘潭大学计算机科学学院）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；Institute of AI and Targeted International Communication, Hunan Normal University（湖南师范大学人工智能与定向国际传播研究院）
We have developed an end-to-end, retrosynthesis system, named ChemiRise, that can propose complete retrosynthesis routes for organic compounds rapidly and reliably. The system was trained on a processed patent database of over 3 million organic reactions. Experimental reactions were atom-mapped, clustered, and extracted into reaction templates. We then trained a graph convolutional neural network-based one-step reaction proposer using template embeddings and developed a guiding algorithm on the directed acyclic graph (DAG) of chemical compounds to find the best candidate to explore. The atom-m…
The reaction center consists of atoms in the product whose local properties are not identical to the corresponding atoms in the reactants. Prior studies on reaction center identification are mainly on semi-templated retrosynthesis methods. Moreover, they are limited to single reaction center identification. However, many reaction centers are comprised of multiple bonds or atoms in reality. We refer to it as the multiple reaction center. This paper presents RCsearcher, a unified framework for single and multiple reaction center identification that combines the advantages of the graph neural net…
Retrosynthesis prediction enhanced by in-silico reaction data augmentation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；College of Chemical and Biological Engineering, Zhejiang University（浙江大学化学工程与生物工程学院）
Retrosynthesis prediction is one of the fundamental challenges in organic synthesis. The task is to predict the reactants given a core product. With the advancement of machine learning, computer-aided synthesis planning has gained increasing interest. Numerous methods were proposed to solve this problem with different levels of dependency on additional chemical knowledge. In this paper, we propose Retroformer, a novel Transformer-based architecture for retrosynthesis prediction without relying on any cheminformatics tools for molecule editing. Via the proposed local attention head, the model c…
University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Tencent AI Lab（腾讯人工智能实验室）
The main target of retrosynthesis is to recursively decompose desired molecules into available building blocks. Existing template-based retrosynthesis methods follow a template selection stereotype and suffer from limited training templates, which prevents them from discovering novel reactions. To overcome this limitation, we propose an innovative retrosynthesis prediction framework that can compose novel templates beyond training templates. As far as we know, this is the first method that uses machine learning to compose reaction templates for retrosynthesis prediction. Besides, we propose an…
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Standigm；Samsung Electronics（三星电子）；Samsung Advanced Institute of Technology (SAIT)（三星先进技术研究院）；AITRICS
Retrosynthesis, of which the goal is to find a set of reactants for synthesizing a target product, is an emerging research area of deep learning. While the existing approaches have shown promising results, they currently lack the ability to consider availability (e.g., stability or purchasability) of the reactants or generalize to unseen reaction templates (i.e., chemical reaction rules). In this paper, we propose a new approach that mitigates the issues by reformulating retrosynthesis into a selection problem of reactants from a candidate set of commercially available molecules. To this end, …
Georgia Institute of Technology（佐治亚理工学院）；Galixir Inc.（Galixir公司）；Massachusetts Institute of Technology（麻省理工学院）；Google Research（谷歌研究院）；Ant Financial（蚂蚁集团）
Retrosynthesis is one of the fundamental problems in organic chemistry. The task is to identify reactants that can be used to synthesize a specified product molecule. Recently, computer-aided retrosynthesis is finding renewed interest from both chemistry and computer science communities. Most existing approaches rely on template-based models that define subgraph matching rules, but whether or not a chemical reaction can proceed is not defined by hard decision rules. In this work, we propose a new approach to this task using the Conditional Graph Logic Network, a conditional graphical model bui…
We propose a new model for making generalizable and diverse retrosynthetic reaction predictions. Given a target compound, the task is to predict the likely chemical reactants to produce the target. This generative task can be framed as a sequence-to-sequence problem by using the SMILES representations of the molecules. Building on top of the popular Transformer architecture, we propose two novel pre-training methods that construct relevant auxiliary tasks (plausible reactions) for our problem. Furthermore, we incorporate a discrete latent variable model into the architecture to encourage the m…
Sun Yat-sen University（中山大学）；Wuyi University（五邑大学）
Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes, but at present it is cumbersome and provides results of dissatisfactory quality. In this study, we develop a template-free self-corrected retrosynthesis predictor (SCROP) to perform a retrosynthesis prediction task trained by using the Transformer neural network architecture. In the method, the retrosynthesis planning is converted as a machine translation problem between molecular linear notations o…
RetroMPA: A Molecular Property-Aware Auxiliary Framework for Enhancing Retrosynthesis Prediction
School of Cyber Science and Engineering, Wuhan University（武汉大学网络安全学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；College of Chemistry and Molecular Sciences, Wuhan University（武汉大学化学与分子科学学院）
Peking University（北京大学）；Shanghai Jiao Tong University（上海交通大学）；National Research Council Canada（加拿大国家研究委员会）；Montréal Institute for Learning Algorithms (MILA)（蒙特利尔学习算法研究所）；Canadian Institute for Advanced Research (CIFAR)（加拿大高等研究院）；HEC Montréal（蒙特利尔高等商学院）
A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule, a.k.a. retrosynthesis prediction. Existing state-of-the-art methods rely on matching the target molecule with a large set of reaction templates, which are very computationally expensive and also suffer from the problem of coverage. In this paper, we propose a novel template-free approach called G2Gs by transforming a target molecular graph into a set of reactant molecular graphs. G2Gs first splits the target molecular graph into a set of synthons by identifying the reaction centers, …
Helmholtz Zentrum München（亥姆霍兹慕尼黑中心）；Institute of Structural Biology（结构生物学研究所）；BigChem GmbH（BigChem有限公司）；Firmenich International SA（芬美意国际公司）
We investigated the effect of different training scenarios on predicting the (retro)synthesis of chemical compounds using a text-like representation of chemical reactions (SMILES) and Natural Language Processing neural network Transformer architecture. We showed that data augmentation, which is a powerful method used in image processing, eliminated the effect of data memorization by neural networks, and improved their performance for the prediction of new sequences. This effect was observed when augmentation was used simultaneously for input and the target data simultaneously. The top-5 accura…
URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment
Insilico Medicine AI Limited（Insilico Medicine AI有限公司）；Independent researcher（独立研究者）；Insilico Medicine Canada Inc.（Insilico Medicine加拿大公司）；Insilico Medicine Hong Kong Ltd.（Insilico Medicine香港有限公司）
RxnNano:Training Compact LLMs for Chemical Reaction and Retrosynthesis Prediction via Hierarchical Curriculum Learning
The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Southeast University（东南大学）；Zhejiang University（浙江大学）
Models Matter: The Impact of Single-Step Retrosynthesis on Synthesis Planning
Helmholtz Zentrum München（慕尼黑亥姆霍兹中心）；Janssen Research & Development, Janssen Pharmaceutica N.V（杨森研究与开发，杨森制药）；Leiden University（莱顿大学）；Pfizer Worldwide Research Development and Medical（辉瑞全球研究开发与医学）；AstraZeneca（阿斯利康）
Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule. A key consideration in building neural models for this task is aligning model design with strategies adopted by chemists. Building on this viewpoint, 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. The model first predicts the set of graph edits transforming the target into incomplete molecules called sy…
The Institute of Statistical Mathematics, Research Organization of Information and Systems（信息系统研究机构统计数学研究所）；Daicel Corporation（大赛璐公司）
The identification of synthetic routes that end with a desired product has been an inherently time-consuming process that is largely dependent on expert knowledge regarding a limited fraction of the entire reaction space. At present, emerging machine-learning technologies are overturning the process of retrosynthetic planning. The objective of this study is to discover synthetic routes backwardly from a given desired molecule to commercially available compounds. The problem is reduced to a combinatorial optimization task with the solution space subject to the combinatorial complexity of all po…
Recently, template-based (TB) and template-free (TF) molecule graph learning methods have shown promising results to retrosynthesis. TB methods are more accurate using pre-encoded reaction templates, and TF methods are more scalable by decomposing retrosynthesis into subproblems, i.e., center identification and synthon completion. To combine both advantages of TB and TF, we suggest breaking a full-template into several semi-templates and embedding them into the two-step TF framework. Since many semi-templates are reduplicative, the template redundancy can be reduced while the essential chemica…
Accelerating the inference of string generation-based chemical reaction models for industrial applications
IDSIA（IDSIA研究所）；USI（瑞士意大利语区大学）；SUPSI（瑞士应用科学大学南部）；Machine Learning Research, Pfizer Research and Development（辉瑞研发部机器学习研究部门）；Institute for Digital Technologies for Personalized Healthcare, SUPSI（瑞士应用科学大学南部个性化医疗数字技术研究所）；AI Initiative, KAUST（阿卜杜拉国王科技大学人工智能倡议）
Chemist-aligned retrosynthesis by ensembling diverse inductive bias models
Microsoft Research AI for Science（微软研究院人工智能与科学研究中心）；Novartis Biomedical Research（诺华生物医学研究）；University of Cambridge（剑桥大学）；Jagiellonian University（雅盖隆大学）
Johannes Kepler University Linz（约翰内斯·开普勒大学林茨分校）；Institute of Advanced Research in Artificial Intelligence (IARAI)（人工智能高级研究所）；Janssen Research & Development, Janssen Pharmaceutica N.V.（杨森制药有限公司杨森研发部）；Microsoft Research（微软研究院）
Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials. To find such routes, computer-assisted synthesis planning (CASP) methods are employed which rely on a model of chemical reactivity. In this study, we model single-step retrosynthesis in a template-based approach using modern Hopfield networks (MHNs). We adapt MHNs to associate different modalities, reaction templates and molecules, which allows the model to leverage structural information about reaction templates. This approach significantly improves the performance of template …
MechRetro is a chemical-mechanism-driven graph learning framework for interpretable retrosynthesis prediction and pathway planning
School of Software, Shandong University（山东大学软件学院）；Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University（山东大学-新加坡南洋理工大学人工智能联合研究中心（C-FAIR））；Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China（电子科技大学基础与前沿研究院）
Copy-Augmented Representation for Structure Invariant Template-Free Retrosynthesis
School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）；Shanghai Institute of Artificial Intelligence for Education, East China Normal University（东华大学上海人工智能教育研究所）
LLM-Augmented Chemical Synthesis and Design Decision Programs
École Polytechnique Fédérale de Lausanne (EPFL)（瑞士联邦理工学院洛桑校区）；National Centre of Competence in Research (NCCR) Catalysis（催化领域竞争力研究国立中心）；Harvard University（哈佛大学）；Cornell University（康奈尔大学）
SynBridge: Bridging Reaction States via Discrete Flow for Bidirectional Reaction Prediction
AI for Science Institute（人工智能科学研究院）；DP Technology（DP科技）；College of Chemistry and Molecular Engineering（化学与分子工程学院）；School of Mathematical Sciences（数学科学学院）；Center for Machine Learning Research（机器学习研究中心）
Centre of New Technologies, University of Warsaw（华沙大学新技术中心）；Institute of Organic Chemistry, Polish Academy of Sciences（波兰科学院有机化学研究所）
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 p…
