Retrosynthesis analysis is pivotal yet challenging in drug discovery and organic chemistry. Despite the proliferation of computational tools over the past decade, AI-based systems often fall short in generalizing across diverse reaction types and exploring alternative synthetic pathways. This paper presents BatGPT-Chem, a large language model with 15 billion parameters, tailored for enhanced retrosynthesis prediction. Integrating chemical tasks via a unified framework of natural language and SMILES notation, this approach synthesizes extensive instructional data from an expansive chemical database. Employing both autoregressive and bidirectional training techniques across over one hundred million instances, BatGPT-Chem captures a broad spectrum of chemical knowledge, enabling precise prediction of reaction conditions and exhibiting strong zero-shot capabilities. Superior to existing AI methods, our model demonstrates significant advancements in generating effective strategies for complex molecules, as validated by stringent benchmark tests. BatGPT-Chem not only boosts the efficiency and creativity of retrosynthetic analysis but also establishes a new standard for computational tools in synthetic design. This development empowers chemists to adeptly address the synthesis of novel compounds, potentially expediting the innovation cycle in drug manufacturing and materials science. We release our trial platform at \url{https://www.batgpt.net/dapp/chem}.
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）
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）
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）
Atom-anchored LLMs speak Chemistry: A Retrosynthesis Demonstration
Machine Learning Research（机器学习研究）；Pfizer Research and Development（辉瑞研发）；Leiden Institute of Advanced Computer Science（莱顿高级计算机科学研究所）；Leiden University（莱顿大学）；Leiden Academic Centre for Drug Research（莱顿药物研究中心）；Leiden Institute of Chemistry（莱顿化学研究所）
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）
RetroDFM-R: Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning
X-LANCE Lab, School of Computer Science MoE Key Lab of Artificial Intelligence SJTU AI Institute（X-LANCE实验室，计算机科学学院，人工智能关键实验室，SJTU人工智能研究所）；Suzhou Laboratory（苏州实验室）；Jiangsu Key Lab of Language Computing（江苏语言计算重点实验室）；School of Chemistry and Chemical Engineering（化学与化工学院）
Institute of Organic Chemistry（有机化学研究所）；Center for Multiscale Theory and Computation（多尺度理论与计算中心）
From medicines to materials, small organic molecules are indispensable for human well-being. To plan their syntheses, chemists employ a problem solving technique called retrosynthesis. In retrosynthesis, target molecules are recursively transformed into increasingly simpler precursor compounds until a set of readily available starting materials is obtained. Computer-aided retrosynthesis would be a highly valuable tool, however, past approaches were slow and provided results of unsatisfactory quality. Here, we employ Monte Carlo Tree Search (MCTS) to efficiently discover retrosynthetic routes. …
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）
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）
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…
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）
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…
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 …
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…
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 is a technique to plan the chemical synthesis of organic molecules, for example drugs, agro- and fine chemicals. In retrosynthesis, a search tree is built by analysing molecules recursively and dissecting them into simpler molecular building blocks until one obtains a set of known building blocks. The search space is intractably large, and it is difficult to determine the value of retrosynthetic positions. Here, we propose to model retrosynthesis as a Markov Decision Process. In combination with a Deep Neural Network policy learned from essentially the complete published knowled…
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…
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）
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…
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）
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）
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 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…
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…
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…
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）
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…
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…
AOT*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Search
School of Data Science, CUHK-Shenzhen（CUHK-Shenzhen数据科学学院）；Shanghai AI Laboratory（上海人工智能实验室）；Jilin University（吉林大学）；Mila - Quebec AI Institute（魁北克AI研究所）；HEC Montréal（蒙特利尔HEC）
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）
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…
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…
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…
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, …
Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Jiao Tong University（上海交通大学）；Fudan University（复旦大学）；University of Sydney（悉尼大学）；Nanjing University（南京大学）；University of Oxford（牛津大学）；The University of Science and Technology of China（中国科学技术大学）；Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences（药物发现与设计中心、国家药物研究重点实验室、上海中医药材料医学研究所、中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Stanford University（斯坦福大学）
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…
ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
Mila-Québec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）；Arizona State University（亚利桑那州立大学）；Princeton University（普林斯顿大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Université de Montréal（蒙特利尔大学）；National Research Council Canada（加拿大国家研究委员会）
