Výsledky vyhledávání - "Computer-aided synthesis planning"

Generate what you can make: achieving in-house synthesizability with readily available resources in de novo drug design

Autoři: Alan Kai Hassen Martin Šícho Yorick J. van Aalst a další

Zdroj: Journal of Cheminformatics, Vol 17, Iss 1, Pp 1-16 (2025)

Témata: Computer-aided synthesis planning, Casp, Retrosynthesis, Synthesizability, Synthesizability score, De novo drug design, Information technology, T58.5-58.64, Chemistry, QD1-999

Popis souboru: electronic resource

Relation: https://doaj.org/toc/1758-2946

Přístupová URL adresa: https://doaj.org/article/f18cefe04320453db1f0f3f967700191

Improving route development using convergent retrosynthesis planning

Autoři: Paula Torren-Peraire Jonas Verhoeven Dorota Herman a další

Zdroj: Journal of Cheminformatics, Vol 17, Iss 1, Pp 1-15 (2025)

Témata: Computer-aided synthesis planning, Convergent routes, Retrosynthesis prediction, Information technology, T58.5-58.64, Chemistry, QD1-999

Popis souboru: electronic resource

Relation: https://doaj.org/toc/1758-2946

Přístupová URL adresa: https://doaj.org/article/e38c38c73b9541c093e01e6aac70467b

CLAIRE: a contrastive learning-based predictor for EC number of chemical reactions

Autoři: Zishuo Zeng Jin Guo Jiao Jin a další

Zdroj: Journal of Cheminformatics, Vol 17, Iss 1, Pp 1-9 (2025)

Témata: Reaction EC number, Contrastive learning, Reaction embeddings, Metabolic model, Computer-aided synthesis planning, Information technology, T58.5-58.64, Chemistry, QD1-999

Popis souboru: electronic resource

Relation: https://doaj.org/toc/1758-2946

Přístupová URL adresa: https://doaj.org/article/72acf5f8d83d49678b1e525df58b7a56

AiZynthFinder 4.0: developments based on learnings from 3 years of industrial application

Autoři: Lakshidaa Saigiridharan Alan Kai Hassen Helen Lai a další

Zdroj: Journal of Cheminformatics, Vol 16, Iss 1, Pp 1-11 (2024)

Témata: Computer-aided synthesis planning, Retrosynthesis software, Multi-step retrosynthesis, Open-source, Information technology, T58.5-58.64, Chemistry, QD1-999

Popis souboru: electronic resource

Relation: https://doaj.org/toc/1758-2946

Přístupová URL adresa: https://doaj.org/article/e108db6d41a54ae9911ac63540ba4e54

Generate What You Can Make: Achieving in-house synthesizability with readily available resources in de novo drug design: achieving in-house synthesizability with readily available resources in de novo drug design

Autoři: Alan Kai Hassen Martin Šícho Yorick J. van Aalst a další

Zdroj: J Cheminform
Journal of Cheminformatics, Vol 17, Iss 1, Pp 1-16 (2025)
Journal of Cheminformatics

Témata: Virtual screening, Synthesizability, Research, Medicinal chemistry, Information technology, T58.5-58.64, Retrosynthesis, Chemistry, In vitro, Computer-aided synthesis planning, Casp, De novo drug design, QD1-999, Synthesizability score

Popis souboru: application/pdf

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/40155970
https://doaj.org/article/f18cefe04320453db1f0f3f967700191
https://hdl.handle.net/1887/4214915

LinChemIn: SynGraph—a data model and a toolkit to analyze and compare synthetic routes

Autoři: Marta Pasquini Marco Stenta

Zdroj: Journal of Cheminformatics, Vol 15, Iss 1, Pp 1-17 (2023)

Témata: Reaction, Chemoinformatics, Computer-aided synthesis planning, Information technology, T58.5-58.64, Chemistry, QD1-999

Popis souboru: electronic resource

Relation: https://doaj.org/toc/1758-2946

Přístupová URL adresa: https://doaj.org/article/3f8f033a384444598f8950d4b03e88b7

Improving the performance of models for one-step retrosynthesis through re-ranking

Autoři: Min Htoo Lin Zhengkai Tu Connor W. Coley

Zdroj: Journal of Cheminformatics, Vol 14, Iss 1, Pp 1-13 (2022)

Témata: Computer-aided synthesis planning, Energy-based model, Cheminformatics, Machine learning, Synthetic chemistry, Information technology, T58.5-58.64, Chemistry, QD1-999

Popis souboru: electronic resource

Relation: https://doaj.org/toc/1758-2946

Přístupová URL adresa: https://doaj.org/article/80f52e3998dd4476b552b4e988f5918e

Training data, trained models and other required files for 'A User-Tunable Machine Learning Framework for Step-Wise Synthesis Planning'.

Autoři: Shivesh Prakash Hans-Arno Jacobsen Viki Kumar Prasad

Témata: Organic chemical synthesis, Computational chemistry, Deep learning, Neural networks, Retrosynthetic Planning, Modern Hopfield Networks, Computer-aided Synthesis Planning

Dostupnost: https://doi.org/10.6084/m9.figshare.28673540.v3
https://figshare.com/articles/dataset/Training_data_trained_models_and_other_required_files_for_A_User-Tunable_Machine_Learning_Framework_for_Step-Wise_Synthesis_Planning_/28673540

Automatic Extraction of Reaction Templates for Synthesis Prediction

Autoři: Amol Thakkar Jean-Louis Reymond

Zdroj: CHIMIA, Vol 76, Iss 4 (2022)

Témata: Computer Aided Synthesis Planning, Computational Chemistry, Reaction informatics, Retrosynthesis, Chemistry, QD1-999

Popis souboru: electronic resource

Relation: https://chimia.ch/chimia/article/view/5893; https://doaj.org/toc/0009-4293; https://doaj.org/toc/2673-2424

Přístupová URL adresa: https://doaj.org/article/70c2e54661c24bb3b84aeff52a2293de

有機合成化学の知見を統合したAI駆動型合成経路設計手法の開発

Autoři: 石田, 祥一 高須, 清誠 石濱, 泰 a další

Přispěvatelé: 石田, 祥一 高須, 清誠 石濱, 泰 a další

Témata: computer-aided synthesis planning, artificial intelligence, retrosynthetic analysis, graph convolutional networks, monte-carlo tree search

Popis souboru: application/pdf

Přístupová URL adresa: https://ndlsearch.ndl.go.jp/books/R100000039-I12219469

Degree: 博士(薬学) -- 京都大学, Kyoto University

Artificial Intelligence for Computer-Aided Synthesis In Flow: Analysis and Selection of Reaction Components

Autoři: Plehiers, Pieter Coley, Connor Goa, Hanyu a další

Zdroj: ISSN:2673-2718 ; Frontiers in Chemical Engineering, vol. 2, Art.No. ARTN 5.

Témata: Science & Technology, Life Sciences & Biomedicine, Technology, Biotechnology & Applied Microbiology, Engineering, Chemical, continuous synthesis, computer-aided synthesis planning, artificial neural networks, gaussian mixture model, reaction conditions, PHASE BECKMANN REARRANGEMENT, DRUG DEVELOPMENT, SYNTHESIS DESIGN, MACHINE, TOOL, RETROSYNTHESIS, METHODOLOGY, PREDICTION, DISCOVERY, CHEMISTRY

Popis souboru: application/pdf

Relation: https://lirias.kuleuven.be/handle/20.500.12942/713827; https://doi.org/10.3389/fceng.2020.00005

Dostupnost: https://lirias.kuleuven.be/handle/20.500.12942/713827
https://hdl.handle.net/20.500.12942/713827
https://lirias.kuleuven.be/retrieve/ea029728-204f-4a7f-9c41-4cafa4645d52
https://doi.org/10.3389/fceng.2020.00005

A Transformer Model for Retrosynthesis

Autoři: Pavel Karpov Guillaume Godin Igor V. Tetko

Zdroj: Lecture Notes in Computer Science ISBN: 9783030304928
Lect. Notes Comput. Sc. 11731 LNCS, 817-830 (2019)
Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions-28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions

Témata: 0301 basic medicine, 0303 health sciences, 03 medical and health sciences, Character-based Models, Computer Aided Synthesis Planning, Retrosynthesis Prediction, Transformer

Popis souboru: application/pdf

Přístupová URL adresa: https://zenodo.org/record/3515044/files/Karpov2019_Chapter_ATransformerModelForRetrosynth.pdf
https://chemrxiv.org/engage/chemrxiv/article-details/60c7417af96a00cd49286446
https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60c7417af96a00cd49286446/original/a-transformer-model-for-retrosynthesis.pdf
https://link.springer.com/chapter/10.1007%2F978-3-030-30493-5_78
https://dblp.uni-trier.de/db/conf/icann/icann2019w.html#KarpovGT19
https://rd.springer.com/chapter/10.1007/978-3-030-30493-5_78
https://push-zb.helmholtz-muenchen.de/frontdoor.php?source_opus=57113

Artificial Intelligence for Chemical Synthesis: Improving the Workflow of Medicinal Chemists using Computer-Aided Synthesis Planning

Autoři: Haywood, Alexe L.

Témata: machine learning, Computer-Aided Synthesis Planning, drug discovery, chemical synthesis

Popis souboru: application/pdf

Relation: https://eprints.nottingham.ac.uk/77169/1/HaywoodAlexe_Thesis.pdf; Haywood, Alexe L. (2024) Artificial Intelligence for Chemical Synthesis: Improving the Workflow of Medicinal Chemists using Computer-Aided Synthesis Planning. PhD thesis, University of Nottingham.

Dostupnost: https://eprints.nottingham.ac.uk/77169/
https://eprints.nottingham.ac.uk/77169/1/HaywoodAlexe_Thesis.pdf

Development of an AI-Driven Organic Synthesis Planning Approach with Retrosynthesis Knowledge

有機合成化学の知見を統合したAI駆動型合成経路設計手法の開発

Autoři: Ishida, Shoichi

Thesis Advisors: 石田, 祥一, イシダ, ショウイチ

Témata: computer-aided synthesis planning, artificial intelligence, retrosynthetic analysis, graph convolutional networks, monte-carlo tree search, 499

Dostupnost: http://hdl.handle.net/2433/263605

A transformer model for retrosynthesis.

Autoři: Karpov, P. Godin, G. Tetko, I.V.

Zdroj: Lect. Notes Comput. Sc. 11731 LNCS, 817-830 (2019)

Témata: Character-based Models, Computer Aided Synthesis Planning, Retrosynthesis Prediction, Transformer

Relation: info:eu-repo/semantics/altIdentifier/isbn/0302-9743; info:eu-repo/semantics/altIdentifier/pissn/0302-9743; info:eu-repo/semantics/altIdentifier/eissn/1611-3349

Dostupnost: https://push-zb.helmholtz-munich.de/frontdoor.php?source_opus=57113
https://doi.org/10.1007/978-3-030-30493-5_78

RPSubAlign: a novel sequence-based molecular representation method for retrosynthesis prediction with improved validity and robustness.

Autoři: Hu Y Hu F Zhang H a další

Zdroj: Briefings in bioinformatics [Brief Bioinform] 2025 May 01; Vol. 26 (3).

Způsob vydávání: Journal Article

Informace o časopise: Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN: 1477-4054 (Electronic) Linking ISSN: 14675463 NLM ISO Abbreviation: Brief Bioinform Subsets: MEDLINE

Výrazy ze slovníku MeSH: Software* , Algorithms* , Computational Biology*/methods , Drug Discovery*/methods

Development of an AI-Driven Organic Synthesis Planning Approach with Retrosynthesis Knowledge.

Autoři: Ishida, Shoichi

Témata: computer-aided synthesis planning, artificial intelligence, retrosynthetic analysis, graph convolutional networks, monte-carlo tree search, 499, Thesis or Dissertation., VoR.

URL: http://hdl.handle.net/2433/263605
https://doi.org/10.14989/doctor.k23144
http://repository.kulib.kyoto-u.ac.jp/dspace/bitstream/2433/263605/1/dyakk00844.pdf
http://repository.kulib.kyoto-u.ac.jp/dspace/bitstream/2433/263605/2/yyakk00844.pdf

Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges.

Autoři: Thomas M Boardman A Garcia-Ortegon M a další

Zdroj: Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2022; Vol. 2390, pp. 1-59.

Způsob vydávání: Journal Article; Research Support, Non-U.S. Gov't; Review

Informace o časopise: Publisher: Humana Press Country of Publication: United States NLM ID: 9214969 Publication Model: Print Cited Medium: Internet ISSN: 1940-6029 (Electronic) Linking ISSN: 10643745 NLM ISO Abbreviation: Methods Mol Biol Subsets: MEDLINE

Výrazy ze slovníku MeSH: Artificial Intelligence* , Drug Design*

Artificial Intelligence for Chemical Synthesis: Improving the Workflow of Medicinal Chemists using Computer-Aided Synthesis Planning

Autoři: Haywood, Alexe L.

Témata: machine learning, Computer-Aided Synthesis Planning, drug discovery, chemical synthesis, Thesis (University of Nottingham only), NonPeerReviewed

URL: https://eprints.nottingham.ac.uk/77169/
https://eprints.nottingham.ac.uk/77169