A Graph to Graphs Framework for Retrosynthesis Prediction
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）
Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang
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, and then translates the synthons to the final reactant graphs via a variational graph translation framework. Experimental results show that G2Gs significantly outperforms existing template-free approaches by up to 63% in terms of the top-1 accuracy and achieves a performance close to that of state-of-the-art template based approaches, but does not require domain knowledge and is much more scalable.
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…
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…
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…
Retrosynthesis is a problem to infer reactant compounds to synthesize a given product compound through chemical reactions. Recent studies on retrosynthesis focus on proposing more sophisticated prediction models, but the dataset to feed the models also plays an essential role in achieving the best generalizing models. Generally, a dataset that is best suited for a specific task tends to be small. In such a case, it is the standard solution to transfer knowledge from a large or clean dataset in the same domain. In this paper, we conduct a systematic and intensive examination of data transfer ap…
University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Tsinghua University（清华大学）；Tencent AI Lab（腾讯人工智能实验室）；Sun Yat-sen University（中山大学）
Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate or assist in the retrosynthesis analysis, various retrosynthesis prediction algorithms have been proposed. However, most of them are cumbersome and lack interpretability about their predictions. In this paper, we devise a novel template-free algorithm for automatic retrosynthetic expansion inspired by how chemists approach retrosynthesis prediction. Our method disassembles retrosynthesis into two step…
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…
RetroOOD: Understanding Out-of-Distribution Generalization in Retrosynthesis Prediction
City University of Hong Kong（香港城市大学）；Zhejiang University（浙江大学）；Nanyang Technological University（南洋理工大学）；Hong Kong University of Science and Technology（香港科技大学）；Shanghai Institute for Advanced Study of Zhejiang University（浙江大学上海高等研究院）
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…
Tencent AI Lab（腾讯人工智能实验室）；Shenzhen University（深圳大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）
Retrosynthesis is a major task for drug discovery. It is formulated as a graph-generating problem by many existing approaches. Specifically, these methods firstly identify the reaction center, and break target molecule accordingly to generate synthons. Reactants are generated by either adding atoms sequentially to synthon graphs or directly adding proper leaving groups. However, both two strategies suffer since adding atoms results in a long prediction sequence which increases generation difficulty, while adding leaving groups can only consider the ones in the training set which results in poo…
Mila（米拉研究所）；Université de Montréal（蒙特利尔大学）；Westfälische Wilhelms-Universität Münster（明斯特大学）；McGill University（麦吉尔大学）；Jagiellonian University（雅盖隆大学）
De novo molecule generation often results in chemically unfeasible molecules. A natural idea to mitigate this problem is to bias the search process towards more easily synthesizable molecules using a proxy for synthetic accessibility. However, using currently available proxies still results in highly unrealistic compounds. We investigate the feasibility of training deep graph neural networks to approximate the outputs of a retrosynthesis planning software, and their use to bias the search process. We evaluate our method on a benchmark involving searching for drug-like molecules with antibiotic…
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）
Leiden University（莱顿大学）；Helmholtz Zentrum München（慕尼黑亥姆霍兹中心）；AstraZeneca（阿斯利康）；Janssen Research & Development（杨森研发）；Janssen Pharmaceutica N.V（杨森制药有限公司）
Retrosynthesis is the task of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found. Consequently, the goal is to provide a valid synthesis route for a molecule. As more single-step models develop, we see increasing accuracy in the prediction of molecular disconnections, potentially improving the creation of synthetic paths. Multi-step approaches repeatedly apply the chemical information stored in single-step retrosynthesis models. However, this connection is not reflected in contemporary research, fixing e…
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）
Retrosynthesis prediction is one of the fundamental challenges in organic chemistry and related fields. The goal is to find reactants molecules that can synthesize product molecules. To solve this task, we propose a new graph-to-graph transformation model, G2GT, in which the graph encoder and graph decoder are built upon the standard transformer structure. We also show that self-training, a powerful data augmentation method that utilizes unlabeled molecule data, can significantly improve the model's performance. Inspired by the reaction type label and ensemble learning, we proposed a novel wea…
We cast retrosynthesis as a machine translation problem by introducing a special Tensor2Tensor, an entire attention-based and fully data-driven model. Given a data set comprising about 50,000 diverse reactions extracted from USPTO patents, the model significantly outperforms seq2seq model (34.7%) on a top-1 accuracy by achieving 54.1%. For yielding better results, parameters such as batch size and training time are thoroughly investigated to train the model. Additionally, we offer a novel insight into the causes of grammatically invalid SMILES, and conduct a test in which experienced chemists …
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…
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…
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…
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）
RetroReasoner: A Reasoning LLM for Strategic Retrosynthesis Prediction
Department of Artificial Intelligence, Korea University（韩国大学人工智能系）；Department of Statistics, Korea University（韩国大学统计系）；Materials Intelligence Lab, LG AI Research（LG人工智能研究实验室）
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）
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 …
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）
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…
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, …
IBM Research – Zurich（IBM苏黎世研究院）；Università di Pisa（比萨大学）
We present an extension of our Molecular Transformer architecture combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention. The single-step retrosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts for each retrosynthetic step. We introduce new metrics (coverage, class diversity, round-trip accuracy and Jensen-Shannon divergence) to evaluate the single-step retrosynthetic models, using the forward prediction and a reaction classification model always based on the transform…
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）
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）
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）
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…
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 …
ConfRetro: a 3D-aware template-free method for enhancing retrosynthesis via molecular conformer information
School of Computer Science and Technology, East China Normal University（东华师范大学计算机科学与技术学院）；Shanghai Institute of Artificial Intelligence for Education, East China Normal University（东华师范大学上海人工智能教育研究所）；Shanghai Frontiers Science Center of Molecule Intelligent Syntheses（上海分子智能合成前沿科学中心）
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）
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）
Zhejiang University（浙江大学）；Hangzhou Huadong Medicine Group Pharmaceutical Research Institute（杭州华东医药集团医药研究院）
Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis. A popular computational paradigm formulates synthesis prediction as a sequence-to-sequence translation problem, where the typical SMILES is adopted for molecule representations. However, the general-purpose SMILES neglects the characteristics of chemical reactions, where the molecular graph topology is largely unaltered from reactants to products, resulting in the suboptimal performance of SMILES if straightforwardly applied. In this article, we propose the ro…
University of Science and Technology of China（中国科学技术大学）；Tencent America（腾讯美国）
Predicting molecular properties with data-driven methods has drawn much attention in recent years. Particularly, Graph Neural Networks (GNNs) have demonstrated remarkable success in various molecular generation and prediction tasks. In cases where labeled data is scarce, GNNs can be pre-trained on unlabeled molecular data to first learn the general semantic and structural information before being fine-tuned for specific tasks. However, most existing self-supervised pre-training frameworks for GNNs only focus on node-level or graph-level tasks. These approaches cannot capture the rich informati…
Many practical graph problems, such as knowledge graph construction and drug-drug interaction prediction, require to handle multi-relational graphs. However, handling real-world multi-relational graphs with Graph Neural Networks (GNNs) is often challenging due to their evolving nature, as new entities (nodes) can emerge over time. Moreover, newly emerged entities often have few links, which makes the learning even more difficult. Motivated by this challenge, we introduce a realistic problem of few-shot out-of-graph link prediction, where we not only predict the links between the seen and unsee…
Link prediction is a fundamental problem in graph data analysis. While most of the literature focuses on transductive link prediction that requires all the graph nodes and majority of links in training, inductive link prediction, which only uses a proportion of the nodes and their links in training, is a more challenging problem in various real-world applications. In this paper, we propose a meta-learning approach with graph neural networks for link prediction: Neural Processes for Graph Neural Networks (NPGNN), which can perform both transductive and inductive learning tasks and adapt to patt…
