Zobrazeno 1 - 10
of 130
pro vyhledávání: '"molecular optimization"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-8 (2024)
Abstract Since the advent of computational analysis and visualization of chemical compounds, Computer-Aided Drug Design has made significant contributions to drug discovery. Recently, de novo drug design and molecular optimization have garnered consi
Externí odkaz:
https://doaj.org/article/5ed9a2e453804ed897b994bbf759471c
Autor:
Zachary Fralish, Daniel Reker
Publikováno v:
Beilstein Journal of Organic Chemistry, Vol 20, Iss 1, Pp 2152-2162 (2024)
Active learning allows algorithms to steer iterative experimentation to accelerate and de-risk molecular optimizations, but actively trained models might still exhibit poor performance during early project stages where the training data is limited an
Externí odkaz:
https://doaj.org/article/56a14ea3acea4fdfa03c71281f539b84
Autor:
Jiazhen He, Alessandro Tibo, Jon Paul Janet, Eva Nittinger, Christian Tyrchan, Werngard Czechtizky, Ola Engkvist
Publikováno v:
Journal of Cheminformatics, Vol 16, Iss 1, Pp 1-15 (2024)
Abstract Designing compounds with a range of desirable properties is a fundamental challenge in drug discovery. In pre-clinical early drug discovery, novel compounds are often designed based on an already existing promising starting compound through
Externí odkaz:
https://doaj.org/article/e73ae601958648b29407a92e9414254a
Publikováno v:
Big Data Mining and Analytics, Vol 7, Iss 1, Pp 142-155 (2024)
Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery, which requires the optimization of a specific objective based on satisfying chemical rules. Herein, we aim to optimize the properties of a speci
Externí odkaz:
https://doaj.org/article/d8b0e9f8a84f4fd4aa2e1221577e300a
Publikováno v:
Journal of Cheminformatics, Vol 15, Iss 1, Pp 1-13 (2023)
Abstract Established molecular machine learning models process individual molecules as inputs to predict their biological, chemical, or physical properties. However, such algorithms require large datasets and have not been optimized to predict proper
Externí odkaz:
https://doaj.org/article/7f5d85b226bb4b9c8f51288a57e10259
Publikováno v:
Journal of Cheminformatics, Vol 15, Iss 1, Pp 1-13 (2023)
Abstract Background Structure-constrained molecular generation is a promising approach to drug discovery. The goal of structure-constrained molecular generation is to produce a novel molecule that is similar to a given source molecule (e.g. hit molec
Externí odkaz:
https://doaj.org/article/a703f3d57ffa4430983e383e618cd42f
Akademický článek
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Akademický článek
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Akademický článek
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Autor:
Jiazhen He, Eva Nittinger, Christian Tyrchan, Werngard Czechtizky, Atanas Patronov, Esben Jannik Bjerrum, Ola Engkvist
Publikováno v:
Journal of Cheminformatics, Vol 14, Iss 1, Pp 1-14 (2022)
Abstract Molecular optimization aims to improve the drug profile of a starting molecule. It is a fundamental problem in drug discovery but challenging due to (i) the requirement of simultaneous optimization of multiple properties and (ii) the large c
Externí odkaz:
https://doaj.org/article/305c5561153a4e0a8b809bbb8268b7d8