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Relevant bibliographies by topics / Computer aided retrosynthesis

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Journal articles
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Academic literature on the topic 'Computer aided retrosynthesis'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Computer aided retrosynthesis"

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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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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Abstract:

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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Dissertations / Theses on the topic "Computer aided retrosynthesis"

1

Ishida, Shoichi. "Development of an AI-Driven Organic Synthesis Planning Approach with Retrosynthesis Knowledge." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263605.

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Book chapters on the topic "Computer aided retrosynthesis"

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Wang, W., Q. Liu, L. Zhang, Y. Dong, and J. Du. "Retrosynthesis Pathway Design Using Hybrid Reaction Templates and Group Contribution-Based Thermodynamic Models." In Computer Aided Chemical Engineering. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-323-85159-6.50014-2.

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Hastedt, Friedrich, Klaus Hellgardt, Sophia Yaliraki, Antonio del Rio Chanona, and Dongda Zhang. "Investigating the Reliability and Interpretability of state-of-the-art Machine Learning Frameworks for Chemical Retrosynthesis." In Computer Aided Chemical Engineering. Elsevier, 2024. http://dx.doi.org/10.1016/b978-0-443-28824-1.50452-x.

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Conference papers on the topic "Computer aided retrosynthesis"

1

Otte, Lennart B., Christer Hogstrand, Adil Mardinoglu, and Miao Guo. "Multi-Omics biological embeddings for ML-models." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.136974.

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Machine learning algorithms have led to the development of numerous vector embeddings for biological entities such as metabolites, proteins, genes, and enzymes. However, these embeddings often lack contextual information due to their specialized focus on individual omics. Disease progression and biosynthesis pathways are increasingly understood through complex, multi-layered networks that integrate diverse omics data and intricate signaling and reaction sequences. Capturing these relationships in a meaningful way requires embeddings that account for both functional and multi-modal dependencies

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