Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Alessandra Toniato"'
Autor:
Antonio Cardinale, Alessandro Castrogiovanni, Theophile Gaudin, Joppe Geluykens, Teodoro Laino, Matteo Manica, Daniel Probst, Philippe Schwaller, Aleksandros Sobczyk, Alessandra Toniato, Alain C. Vaucher, Heiko Wolf, Federico Zipoli
Publikováno v:
CHIMIA, Vol 77, Iss 7/8 (2023)
The RXN for Chemistry project, initiated by IBM Research Europe – Zurich in 2017, aimed to develop a series of digital assets using machine learning techniques to promote the use of data-driven methodologies in synthetic organic chemistry. This res
Externí odkaz:
https://doaj.org/article/a7d5d4b484f54709884821f7a8e58eba
Publikováno v:
CHIMIA, Vol 77, Iss 3 (2023)
Sustainability is here to stay. As businesses migrate away from fossil fuels and toward renewable sources, chemistry will play a crucial role in bringing the economy to a point of net-zero emissions. In fact, chemistry has always been at the forefron
Externí odkaz:
https://doaj.org/article/5d354490b7d2403aae9d74e75eb1eabe
Autor:
Miruna T Cretu, Alessandra Toniato, Amol Thakkar, Amin A Debabeche, Teodoro Laino, Alain C Vaucher
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 3, p 035014 (2023)
With the growing amount of chemical data stored digitally, it has become crucial to represent chemical compounds accurately and consistently. Harmonized representations facilitate the extraction of insightful information from datasets, and are advant
Externí odkaz:
https://doaj.org/article/940a57d4fb2c4fa6b80bc83dd1673912
Publikováno v:
Digital Discovery. 2:489-501
Current Al solutions to chemical retrosynthesis focus on predicting the reported ground truth, not taking into account the ability to generate alternatives. Our work is the first Al approach tackling and analysing retrosynthetic diversity directly.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::88f38384833a3cdc0b2a7d2479c19698
https://doi.org/10.1039/d2dd00110a
https://doi.org/10.1039/d2dd00110a
Autor:
Miruna T. Cretu, Alessandra Toniato, Alain C. Vaucher, Amol Thakkar, Amin Debabeche, Teodoro Laino
With the growing amount of chemical data stored digitally, it has become crucial to represent chemical compounds consistently. Harmonized representations facilitate the extraction of insightful information from datasets, and are advantageous for mach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ce598e2198359818f62129926a8bfc7f
https://doi.org/10.26434/chemrxiv-2022-14ztf
https://doi.org/10.26434/chemrxiv-2022-14ztf
Autor:
Alessandra Toniato, Jan P. Unsleber, Alain C. Vaucher, Thomas Weymuth, Daniel Probst, Teodoro Laino, Markus Reiher
Data-driven synthesis planning has seen remarkable successes in recent years by virtue of modern approaches of artificial intelligence that efficiently exploit vast databases with experimental data on chemical reactions. However, this success story i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bc5048a192d5378e151e41595967af88
https://doi.org/10.26434/chemrxiv-2022-gd0q9
https://doi.org/10.26434/chemrxiv-2022-gd0q9
Autor:
Amol Thakkar, Alain Vaucher, Andrea Byekwaso, Philippe Schwaller, Alessandra Toniato, Teodoro Laino
Data-driven approaches to retrosynthesis have thus far been limited in user interaction, in the diversity of their predictions, and the recommendation of unintuitive disconnection strategies. Herein, we extend the notions of prompt- based inference i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d38554e86b39350d76a68c37a22d77e5
https://doi.org/10.26434/chemrxiv-2022-gx9gb
https://doi.org/10.26434/chemrxiv-2022-gx9gb
Publikováno v:
Catalysis Today. 387:140-142
Today, about 90% of all chemical processes employ in some stage catalysis with an enormous potential for additional impact in areas like energy, fuels, and production of high volume chemicals. The numerous societal challenges are demanding a dramatic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e3238c399835445c10e0928a9a2cc373
https://doi.org/10.1016/j.cattod.2021.10.001
https://doi.org/10.1016/j.cattod.2021.10.001
Publikováno v:
Nature Machine Intelligence. 3:485-494
Existing deep learning models applied to reaction prediction in organic chemistry can reach high levels of accuracy (>90% for natural language processing-based ones). With no chemical knowledge embedded other than the information learnt from reaction
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02db1cf948560170132531e7c1666c9b
https://doi.org/10.1038/s42256-021-00319-w
https://doi.org/10.1038/s42256-021-00319-w
Retrosynthesis is an approach commonly undertaken when considering the manufacture of novel molecules. During this process, a target molecule is broken down and analyzed by considering the bonds to be changed as well as the functional group interconv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08b86520e129ba3baff51ac14c966589
https://doi.org/10.26434/chemrxiv-2021-7hp1s
https://doi.org/10.26434/chemrxiv-2021-7hp1s