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Journal articles on the topic 'Computer aided retrosynthesis'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Rother, Dörte, and Stephan Malzacher. "Computer-aided enzymatic retrosynthesis." Nature Catalysis 4, no. 2 (2021): 92–93. http://dx.doi.org/10.1038/s41929-021-00582-5.

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2

Lin, Yingfu, Rui Zhang, Di Wang, and Tim Cernak. "Computer-aided key step generation in alkaloid total synthesis." Science 379, no. 6631 (2023): 453–57. http://dx.doi.org/10.1126/science.ade8459.

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Efficient chemical synthesis is critical to satisfying future demands for medicines, materials, and agrochemicals. Retrosynthetic analysis of modestly complex molecules has been automated over the course of decades, but the combinatorial explosion of route possibilities has challenged computer hardware and software until only recently. Here, we explore a computational strategy that merges computer-aided synthesis planning with molecular graph editing to minimize the number of synthetic steps required to produce alkaloids. Our study culminated in an enantioselective three-step synthesis of (–)-

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3

Nair, Vishnu H., Philippe Schwaller, and Teodoro Laino. "Data-driven Chemical Reaction Prediction and Retrosynthesis." CHIMIA International Journal for Chemistry 73, no. 12 (2019): 997–1000. http://dx.doi.org/10.2533/chimia.2019.997.

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The synthesis of organic compounds, which is central to many areas such as drug discovery, material synthesis and biomolecular chemistry, requires chemists to have years of knowledge and experience. The development of technologies with the potential to learn and support experts in the design of synthetic routes is a half-century-old challenge with an interesting revival in the last decade. In fact, the renewed interest in artificial intelligence (AI), driven mainly by data availability, is profoundly changing the landscape of computer-aided chemical reaction prediction and retrosynthetic analy

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4

Xu, Jiangcheng, Jun Dong, Kui Du, Wenwen Liu, Jiehai Peng, and Wenbo Yu. "RadicalRetro: A Deep Learning-Based Retrosynthesis Model for Radical Reactions." Processes 13, no. 6 (2025): 1792. https://doi.org/10.3390/pr13061792.

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With the rapid development of radical initiation technologies such as photocatalysis and electrocatalysis, radical reactions have become an increasingly attractive approach for constructing target molecules. However, designing efficient synthetic routes using radical reactions remains a significant challenge due to the inherent complexity and instability of radical intermediates. While computer-aided synthesis planning (CASP) has advanced retrosynthetic analysis for polar reactions, radical reactions have been largely overlooked in AI-driven approaches. In this study, we introduce RadicalRetro

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5

Teixeira, Rodolfo I., and Brahim Benyahia. "Design and optimization of a shared synthetic route for multiple active pharmaceutical ingredients through combined computer aided retrosynthesis and flow chemistry." Chemical Engineering Research and Design 216 (April 2025): 367–75. https://doi.org/10.1016/j.cherd.2025.03.004.

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6

Sun, Yijia, and Nikolaos V. Sahinidis. "Computer-aided retrosynthetic design: fundamentals, tools, and outlook." Current Opinion in Chemical Engineering 35 (March 2022): 100721. http://dx.doi.org/10.1016/j.coche.2021.100721.

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7

Hu, Ye, Antonio de la Vega de León, Bijun Zhang, and Jürgen Bajorath. "Matched molecular pair-based data sets for computer-aided medicinal chemistry." F1000Research 3 (February 4, 2014): 36. http://dx.doi.org/10.12688/f1000research.3-36.v1.

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Matched molecular pairs (MMPs) are widely used in medicinal chemistry to study changes in compound properties including biological activity, which are associated with well-defined structural modifications. Herein we describe up-to-date versions of three MMP-based data sets that have originated from in-house research projects. These data sets include activity cliffs, structure-activity relationship (SAR) transfer series, and second generation MMPs based upon retrosynthetic rules. The data sets have in common that they have been derived from compounds included in the latest release of the ChEMBL

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8

Hu, Ye, Antonio de la Vega de León, Bijun Zhang, and Jürgen Bajorath. "Matched molecular pair-based data sets for computer-aided medicinal chemistry." F1000Research 3 (February 21, 2014): 36. http://dx.doi.org/10.12688/f1000research.3-36.v2.

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Matched molecular pairs (MMPs) are widely used in medicinal chemistry to study changes in compound properties including biological activity, which are associated with well-defined structural modifications. Herein we describe up-to-date versions of three MMP-based data sets that have originated from in-house research projects. These data sets include activity cliffs, structure-activity relationship (SAR) transfer series, and second generation MMPs based upon retrosynthetic rules. The data sets have in common that they have been derived from compounds included in the ChEMBL database (release 17)

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9

Kyrychenko, Alexander, Igor Bylov, Anna Geleverya, et al. "Computer-aided rational design and synthesis of new potential antihypertensive agents among 1,2,3-triazole-containing nifedipine analogs." Computer-aided rational design and synthesis of new potential antihypertensive agents among 1,2,3-triazole-containing nifedipine analogs 49, no. 3 (2024): 4–12. https://doi.org/10.15587/2519-4852.2024.291626.

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1,2,3-Triazole-containing Nifedipine analogues offer the opportunity to increase biostability, bioavailability, efficacy and binding selectivity to target receptors. Here, we applied a computer-aided rational design for identifying new Nifedipine analogues containing a 1,2,3-triazole moiety. First, a new chemical library of 796 derivatives combining the DHP fragment and 1,2,3-triazole moiety was generated. Second, to reduce the library size, the library was pre-filtered using two 3D-pharmacophore models with different complexity, which allowed us to gradually reduce the chemical space, ending

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10

Kyrychenko, Alexander, Igor Bylov, Anna Geleverya, et al. "Computer-aided rational design and synthesis of new potential antihypertensive agents among 1,2,3-triazole-containing nifedipine analogs." ScienceRise: Pharmaceutical Science, no. 3 (49) (June 30, 2024): 4–12. http://dx.doi.org/10.15587/2519-4852.2024.291626.

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1,2,3-Triazole-containing Nifedipine analogues offer the opportunity to increase biostability, bioavailability, efficacy and binding selectivity to target receptors. Here, we applied a computer-aided rational design for identifying new Nifedipine analogues containing a 1,2,3-triazole moiety. First, a new chemical library of 796 derivatives combining the DHP fragment and 1,2,3-triazole moiety was generated. Second, to reduce the library size, the library was pre-filtered using two 3D-pharmacophore models with different complexity, which allowed us to gradually reduce the chemical space, ending

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11

Fortunato, Michael E., Connor W. Coley, Brian C. Barnes, and Klavs F. Jensen. "Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning." Journal of Chemical Information and Modeling 60, no. 7 (2020): 3398–407. http://dx.doi.org/10.1021/acs.jcim.0c00403.

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12

Skoraczyński, Grzegorz, Mateusz Kitlas, Błażej Miasojedow, and Anna Gambin. "Critical assessment of synthetic accessibility scores in computer-assisted synthesis planning." Journal of Cheminformatics 15, no. 1 (2023). http://dx.doi.org/10.1186/s13321-023-00678-z.

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AbstractModern computer-assisted synthesis planning tools provide strong support for this problem. However, they are still limited by computational complexity. This limitation may be overcome by scoring the synthetic accessibility as a pre-retrosynthesis heuristic. A wide range of machine learning scoring approaches is available, however, their applicability and correctness were studied to a limited extent. Moreover, there is a lack of critical assessment of synthetic accessibility scores with common test conditions.In the present work, we assess if synthetic accessibility scores can reliably

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13

Kreutter, David, and Jean-Louis Reymond. "Chemoenzymatic multistep retrosynthesis with transformer loops." Chemical Science, 2024. http://dx.doi.org/10.1039/d4sc02408g.

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14

Zheng, Shuangjia, Tao Zeng, Chengtao Li, et al. "Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP." Nature Communications 13, no. 1 (2022). http://dx.doi.org/10.1038/s41467-022-30970-9.

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AbstractThe complete biosynthetic pathways are unknown for most natural products (NPs), it is thus valuable to make computer-aided bio-retrosynthesis predictions. Here, a navigable and user-friendly toolkit, BioNavi-NP, is developed to predict the biosynthetic pathways for both NPs and NP-like compounds. First, a single-step bio-retrosynthesis prediction model is trained using both general organic and biosynthetic reactions through end-to-end transformer neural networks. Based on this model, plausible biosynthetic pathways can be efficiently sampled through an AND-OR tree-based planning algori

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15

Zhong, Zipeng, Jie Song, Zunlei Feng, et al. "Recent advances in deep learning for retrosynthesis." WIREs Computational Molecular Science, October 20, 2023. http://dx.doi.org/10.1002/wcms.1694.

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AbstractRetrosynthesis 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 deep learning 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 introduce their goal and provide a

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16

Kreutter, David, and Jean-Louis Reymond. "Multistep retrosynthesis combining a disconnection aware triple transformer loop with a route penalty score guided tree search." Chemical Science, 2023. http://dx.doi.org/10.1039/d3sc01604h.

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Computer-aided synthesis planning (CASP) aims to automatically learn organic reactivity from literature and perform retrosynthesis of unseen molecules. CASP systems must learn reactions sufficiently precisely to propose realistic disconnections while...

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17

Torren-Peraire, Paula, Jonas Verhoeven, Dorota Herman, Hugo Ceulemans, Igor V. Tetko, and Jörg K. Wegner. "Improving route development using convergent retrosynthesis planning." Journal of Cheminformatics 17, no. 1 (2025). https://doi.org/10.1186/s13321-025-00953-1.

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Abstract Retrosynthesis consists of recursively breaking down a target molecule to produce a synthesis route composed of readily accessible building blocks. In recent years, computer-aided synthesis planning methods have allowed a greater exploration of potential synthesis routes, combining state-of-the-art machine-learning methods with chemical knowledge. However, these methods are generally developed to produce individual routes from a singular product to a set of proposed building blocks and are not designed to leverage potential shared paths between targets. These methods do not necessaril

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18

Ucak, Umit V., Taek Kang, Junsu Ko, and Juyong Lee. "Substructure-based neural machine translation for retrosynthetic prediction." Journal of Cheminformatics 13, no. 1 (2021). http://dx.doi.org/10.1186/s13321-020-00482-z.

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AbstractWith the rapid improvement of machine translation approaches, neural machine translation has started to play an important role in retrosynthesis planning, which finds reasonable synthetic pathways for a target molecule. Previous studies showed that utilizing the sequence-to-sequence frameworks of neural machine translation is a promising approach to tackle the retrosynthetic planning problem. In this work, we recast the retrosynthetic planning problem as a language translation problem using a template-free sequence-to-sequence model. The model is trained in an end-to-end and a fully da

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19

Zhao, Dengwei, Shikui Tu, and Lei Xu. "Efficient retrosynthetic planning with MCTS exploration enhanced A* search." Communications Chemistry 7, no. 1 (2024). http://dx.doi.org/10.1038/s42004-024-01133-2.

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AbstractRetrosynthetic planning, which aims to identify synthetic pathways for target molecules from starting materials, is a fundamental problem in synthetic chemistry. Computer-aided retrosynthesis has made significant progress, in which heuristic search algorithms, including Monte Carlo Tree Search (MCTS) and A* search, have played a crucial role. However, unreliable guiding heuristics often cause search failure due to insufficient exploration. Conversely, excessive exploration also prevents the search from reaching the optimal solution. In this paper, MCTS exploration enhanced A* (MEEA*) s

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20

Zhong, Weihe, Ziduo Yang, and Calvin Yu-Chian Chen. "Retrosynthesis prediction using an end-to-end graph generative architecture for molecular graph editing." Nature Communications 14, no. 1 (2023). http://dx.doi.org/10.1038/s41467-023-38851-5.

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AbstractRetrosynthesis planning, the process of identifying a set of available reactions to synthesize the target molecules, remains a major challenge in organic synthesis. Recently, computer-aided synthesis planning has gained renewed interest and various retrosynthesis prediction algorithms based on deep learning have been proposed. However, most existing methods are limited to the applicability and interpretability of model predictions, and further improvement of predictive accuracy to a more practical level is still required. In this work, inspired by the arrow-pushing formalism in chemica

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21

Li, Junren, Lei Fang, and Jian-Guang Lou. "RetroRanker: leveraging reaction changes to improve retrosynthesis prediction through re-ranking." Journal of Cheminformatics 15, no. 1 (2023). http://dx.doi.org/10.1186/s13321-023-00727-7.

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AbstractRetrosynthesis is an important task in organic chemistry. Recently, numerous data-driven approaches have achieved promising results in this task. However, in practice, these data-driven methods might lead to sub-optimal outcomes by making predictions based on the training data distribution, a phenomenon we refer as frequency bias. For example, in template-based approaches, low-ranked predictions are typically generated by less common templates with low confidence scores which might be too low to be comparable, and it is observed that recorded reactants can be among these low-ranked pre

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22

Mollinga, Joris, and Valeriu Codreanu. "Scaling Out Transformer Models for Retrosynthesis on Supercomputers." July 15, 2021. https://doi.org/10.1007/978-3-030-80119-9_4.

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Retrosynthesis is the task of building a molecule from smaller precursor molecules. As shown in previous work, good results can be achieved on this task with the help of deep learning techniques, for ex- ample with the help of Transformer networks. Here the retrosynthesis task is treated as a machine translation problem where the Transformer network predicts the precursor molecules given a string representation of the target molecule. Previous research has focused on performing the training procedure on a single machine but in this article we investigate the effect of scaling the training of t

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23

Teixeira, Rodolfo I., Michael Andresini, Renzo Luisi, and Brahim Benyahia. "Computer-Aided Retrosynthesis for Greener and Optimal Total Synthesis of a Helicase-Primase Inhibitor Active Pharmaceutical Ingredient." JACS Au, October 2, 2024. http://dx.doi.org/10.1021/jacsau.4c00624.

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24

Lin, Min Htoo, Zhengkai Tu, and Connor W. Coley. "Improving the performance of models for one-step retrosynthesis through re-ranking." Journal of Cheminformatics 14, no. 1 (2022). http://dx.doi.org/10.1186/s13321-022-00594-8.

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Abstract Retrosynthesis is at the core of organic chemistry. Recently, the rapid growth of artificial intelligence (AI) has spurred a variety of novel machine learning approaches for data-driven synthesis planning. These methods learn complex patterns from reaction databases in order to predict, for a given product, sets of reactants that can be used to synthesise that product. However, their performance as measured by the top-N accuracy in matching published reaction precedents still leaves room for improvement. This work aims to enhance these models by learning to re-rank their reactant pred

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25

Wang, Xinqiao, Chuansheng Yao, Yun Zhang, et al. "From theory to experiment: transformer-based generation enables rapid discovery of novel reactions." Journal of Cheminformatics 14, no. 1 (2022). http://dx.doi.org/10.1186/s13321-022-00638-z.

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AbstractDeep learning methods, such as reaction prediction and retrosynthesis analysis, have demonstrated their significance in the chemical field. However, the de novo generation of novel reactions using artificial intelligence technology requires further exploration. Inspired by molecular generation, we proposed a novel task of reaction generation. Herein, Heck reactions were applied to train the transformer model, a state-of-art natural language process model, to generate 4717 reactions after sampling and processing. Then, 2253 novel Heck reactions were confirmed by organizing chemists to j

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26

Ongtanasup, Tassanee, and Komgrit Eawsakul. "Developing Novel Beta‐Secretase Inhibitors in a Computer Model as a Possible Treatment for Alzheimer’s Disease." Advances in Pharmacological and Pharmaceutical Sciences 2025, no. 1 (2025). https://doi.org/10.1155/adpp/5528793.

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Alzheimer’s disease (AD) is a neurological condition that causes neurons and axons in the brain to deteriorate over time and in a specific pattern. The enzyme beta‐secretase‐1 (BACE‐1) plays a crucial role in the onset and progression of AD. In silico approaches, or computer‐aided drug design, have become useful tools for reducing the number of therapeutic candidates that need to be evaluated in human clinical trials. Finding chemicals that bind to BACE‐1’s active site and inhibit its activity is key for preventing AD. A pharmacophore model was developed in this study based on potent BACE‐1 in

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27

Naveja, J. Jesús, B. Angélica Pilón-Jiménez, Jürgen Bajorath, and José L. Medina-Franco. "A general approach for retrosynthetic molecular core analysis." Journal of Cheminformatics 11, no. 1 (2019). http://dx.doi.org/10.1186/s13321-019-0380-5.

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Abstract Scaffold analysis of compound data sets has reemerged as a chemically interpretable alternative to machine learning for chemical space and structure–activity relationships analysis. In this context, analog series-based scaffolds (ASBS) are synthetically relevant core structures that represent individual series of analogs. As an extension to ASBS, we herein introduce the development of a general conceptual framework that considers all putative cores of molecules in a compound data set, thus softening the often applied “single molecule–single scaffold” correspondence. A putative core is

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28

Moukhliss, Youness, Yassine Koubi, Imran Zafar, et al. "Design of novel isoxazole derivatives as tubulin inhibitors using computer-aided techniques: QSAR modeling, in silico ADMETox, molecular docking, molecular dynamics, biological efficacy, and retrosynthesis." Journal of Biomolecular Structure and Dynamics, February 14, 2024, 1–12. http://dx.doi.org/10.1080/07391102.2024.2306493.

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29

Gerges, Amgad, and Una Canning. "High-risk neuroblastoma stage 4 (NBS4): multi-target inhibitors for c-Src kinases (Csk) and retinoic acid (RA) signalling pathways." Exploration of Drug Science 3 (May 9, 2025). https://doi.org/10.37349/eds.2025.1008109.

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Aim: This paper investigates two possible treatment targets for neuroblastoma (NB) stage 4 (NBS4), c-Src kinase (Csk) and retinoic acid (RA) signalling pathways as potential candidates for a multi-target drug. Research has demonstrated that many cancer cells overexpress and/or hyperactivate c-Src, a tyrosine that is a member of the Src-family kinases. In the case of NBS4, there are indications that successful inhibition of c-Src could inhibit disease progression. Research into the altered signalling of RA, which preserves the differentiated state of adult neurons, neural stem cells, and NB cel

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30

Watson, Ian A., Jibo Wang, and Christos A. Nicolaou. "A retrosynthetic analysis algorithm implementation." Journal of Cheminformatics 11, no. 1 (2019). http://dx.doi.org/10.1186/s13321-018-0323-6.

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31

Liu, Jiahan, Chaochao Yan, Yang Yu, et al. "MARS: a motif-based autoregressive model for retrosynthesis prediction." Bioinformatics, February 29, 2024. http://dx.doi.org/10.1093/bioinformatics/btae115.

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Abstract Motivation Retrosynthesis is a critical task in drug discovery, aimed at finding a viable pathway for synthesizing a given target molecule. Many existing approaches frame this task as a graph-generating problem. Specifically, these methods first identify the reaction center, and break a targeted molecule accordingly to generate the synthons. Reactants are generated by either adding atoms sequentially to synthon graphs or by directly adding appropriate leaving groups. However, both of these strategies have limitations. Adding atoms results in a long prediction sequence which increases

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32

Genheden, Samuel, Amol Thakkar, Veronika Chadimová, Jean-Louis Reymond, Ola Engkvist, and Esben Bjerrum. "AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning." Journal of Cheminformatics 12, no. 1 (2020). http://dx.doi.org/10.1186/s13321-020-00472-1.

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AbstractWe present the open-source AiZynthFinder software that can be readily used in retrosynthetic planning. The algorithm is based on a Monte Carlo tree search that recursively breaks down a molecule to purchasable precursors. The tree search is guided by an artificial neural network policy that suggests possible precursors by utilizing a library of known reaction templates. The software is fast and can typically find a solution in less than 10 s and perform a complete search in less than 1 min. Moreover, the development of the code was guided by a range of software engineering principles s

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33

Yoshikawa, Naruki, Ryuichi Kubo, and Kazuki Z. Yamamoto. "Twitter integration of chemistry software tools." Journal of Cheminformatics 13, no. 1 (2021). http://dx.doi.org/10.1186/s13321-021-00527-x.

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AbstractSocial media activity on a research article is considered to be an altmetric, a new measure to estimate research impact. Demonstrating software on Twitter is a powerful way to attract attention from a larger audience. Twitter integration of software can also lower the barriers to trying the tools and make it easier to save and share the output. We present three case studies of Twitter bots for cheminformatics: retrosynthetic analysis, 3D molecule viewer, and 2D chemical structure editor. These bots make software research more accessible to a broader range of people and facilitate the

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34

Abderrahmane, Massina, Hamza Tajmouati, Vinicius Barros Ribeiro da Silva, and Quentin Perron. "Predicting the Price of Molecules Using Their Predicted Synthetic Pathways**." Molecular Informatics 44, no. 2 (2025). https://doi.org/10.1002/minf.202400039.

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AbstractCurrently, numerous metrics allow chemists and computational chemists to refine and filter libraries of virtual molecules in order to prioritize their synthesis. Some of the most commonly used metrics and models are QSAR models, docking scores, diverse druggability metrics, and synthetic feasibility scores to name only a few. To our knowledge, among the known metrics, a function which estimates the price of a novel virtual molecule and which takes into account the availability and price of starting materials has not been considered before in literature. Being able to make such a predic

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35

Parrot, Maud, Hamza Tajmouati, Vinicius Barros Ribeiro da Silva, et al. "Integrating synthetic accessibility with AI-based generative drug design." Journal of Cheminformatics 15, no. 1 (2023). http://dx.doi.org/10.1186/s13321-023-00742-8.

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AbstractGenerative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule “synthesizability”, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), wh

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36

Amano, Kohei, Tsubasa Matsumoto, Kenichi Tanaka, Kimito Funatsu, and Masaaki Kotera. "Metabolic disassembler for understanding and predicting the biosynthetic units of natural products." BMC Bioinformatics 20, no. 1 (2019). http://dx.doi.org/10.1186/s12859-019-3183-9.

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Abstract Background Natural products are the source of various functional materials such as medicines, and understanding their biosynthetic pathways can provide information that is helpful for their effective production through the synthetic biology approach. A number of studies have aimed to predict biosynthetic pathways from their chemical structures in a retrosynthesis manner; however, sometimes the calculation finishes without reaching the starting material from the target molecule. In order to address this problem, the method to find suitable starting materials is required. Results In thi

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