Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Michael Moret"'
Leveraging molecular structure and bioactivity with chemical language models for de novo drug design
Autor:
Michael Moret, Irene Pachon Angona, Leandro Cotos, Shen Yan, Kenneth Atz, Cyrill Brunner, Martin Baumgartner, Francesca Grisoni, Gisbert Schneider
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-12 (2023)
Generative Deep Learning holds promise for mining the unexplored “chemical universe” for new drugs. Here, the authors demonstrate the de novo design of phosphoinositide 3-kinase gamma (PI3Kγ) inhibitors for the PI3K/Akt pathway in human tumor ce
Externí odkaz:
https://doaj.org/article/813fd08e95d44abfaa961a2900ee404a
Author Correction: Automated Gleason grading of prostate cancer tissue microarrays via deep learning
Autor:
Eirini Arvaniti, Kim S. Fricker, Michael Moret, Niels Rupp, Thomas Hermanns, Christian Fankhauser, Norbert Wey, Peter J. Wild, Jan H. Rüschoff, Manfred Claassen
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-1 (2021)
Externí odkaz:
https://doaj.org/article/f76f8f8764ee4c1e88609dc3dccbe4a5
Publikováno v:
PLoS Computational Biology, Vol 15, Iss 8, p e1007242 (2019)
A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes. Currently, available methods for building kinetic models can cope indirectly with uncertainties by integrating data from differen
Externí odkaz:
https://doaj.org/article/cb999c274e5845f08fe3a15966e8750a
Publikováno v:
International Journal of Educational Management and Development Studies, Vol 5, Iss 3, Pp 1-27 (2024)
Numerous middle leadership researchers in education worldwide have conceded the value and the contribution of Departmental Heads (DHs) in enhancing the achievement of the learners. Yet researches generate insufficient insights on this topic work and
Externí odkaz:
https://doaj.org/article/848dba4a7dc64d4292effdfca6c9ba51
Generating large omics datasets has become routine practice to gain insights into cellular processes, yet deciphering such massive datasets and determining intracellular metabolic states remains challenging. Kinetic models of metabolism play a critic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::262c0d15fcf52923959b6e0ef88c565b
https://doi.org/10.1101/2023.02.21.529387
https://doi.org/10.1101/2023.02.21.529387
Publikováno v:
Angewandte Chemie-International Edition, 60(35), 19477-19482. Wiley
Angewandte Chemie. International Edition, 60 (35)
Angewandte Chemie (International Ed. in English)
Angewandte Chemie. International Edition, 60 (35)
Angewandte Chemie (International Ed. in English)
Chemical language models enable de novo drug design without the requirement for explicit molecular construction rules. While such models have been applied to generate novel compounds with desired bioactivity, the actual prioritization and selection o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65672631605aa21ddfeaab51b15d5138
http://www.scopus.com/inward/record.url?scp=85110483940&partnerID=8YFLogxK
http://www.scopus.com/inward/record.url?scp=85110483940&partnerID=8YFLogxK
Publikováno v:
Nature Machine Intelligence, 2 (3)
Generative machine learning models sample molecules from chemical space without the need for explicit design rules. To enable the generative design of innovative molecular entities with limited training data, a deep learning framework for customized
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::470cee91b21b2c0575d88f42026dc5be
https://doi.org/10.1038/s42256-020-0160-y
https://doi.org/10.1038/s42256-020-0160-y
Autor:
Subham Choudhury, Michael Moret, Pierre SALVY, Ljubisa Miskovic, Daniel Robert Weilandt, Vassily Hatzimanikatis
Kinetic models of metabolism relate metabolic fluxes, metabolite concentrations and enzyme levels through mechanistic relations, rendering them essential for understanding, predicting and optimizing the behaviour of living organisms. However, due to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa99d0033e7349996570e02df0ae796c
https://doi.org/10.1101/2022.01.06.475020
https://doi.org/10.1101/2022.01.06.475020
Publikováno v:
Journal of Chemical Information and Modeling, 62 (5)
Journal of Chemical Information and Modeling, 62(5), 1199-1206. American Chemical Society
Journal of Chemical Information and Modeling, 62(5), 1199-1206. American Chemical Society
Chemical language models (CLMs) can be employed to design molecules with desired properties. CLMs generate new chemical structures in the form of textual representations, such as the simplified molecular input line entry system (SMILES) strings. Howe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::27f05a7ebd5b3cc5270af119a1e097ea
https://doi.org/10.33774/chemrxiv-2021-zv6f1
https://doi.org/10.33774/chemrxiv-2021-zv6f1
Generative chemical language models (CLMs) can be used for de novo molecular structure generation. These CLMs learn from the structural information of known molecules to generate new ones. In this paper, we show that “hybrid” CLMs can additionall
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::817706cc80d3090e5b2f7c691f914407
https://doi.org/10.33774/chemrxiv-2021-xzgst
https://doi.org/10.33774/chemrxiv-2021-xzgst