Zobrazeno 1 - 10
of 220
pro vyhledávání: '"C. Vaucher"'
Publikováno v:
Communications Chemistry, Vol 7, Iss 1, Pp 1-11 (2024)
Abstract The application of machine learning models in chemistry has made remarkable strides in recent years. While analytical chemistry has received considerable interest from machine learning practitioners, its adoption into everyday use remains li
Externí odkaz:
https://doaj.org/article/711d178e743248f4920875e7b1d37f85
Autor:
Alain C. Vaucher, Philippe Schwaller, Joppe Geluykens, Vishnu H. Nair, Anna Iuliano, Teodoro Laino
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-1 (2024)
Externí odkaz:
https://doaj.org/article/0c93348d6739468e9910f541187bb5e9
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:
Machine Learning: Science and Technology, Vol 5, Iss 2, p 025071 (2024)
Inferring missing molecules in chemical equations is an important task in chemistry and drug discovery. In fact, the completion of chemical equations with necessary reagents is important for improving existing datasets by detecting missing compounds,
Externí odkaz:
https://doaj.org/article/ae17dbf8eae8470fb98b88bf5465de0c
Autor:
Alain C. Vaucher, Philippe Schwaller, Joppe Geluykens, Vishnu H. Nair, Anna Iuliano, Teodoro Laino
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
Abstract The experimental execution of chemical reactions is a context-dependent and time-consuming process, often solved using the experience collected over multiple decades of laboratory work or searching similar, already executed, experimental pro
Externí odkaz:
https://doaj.org/article/dcccf88e9c3d4e90a2cd8b02fcfe56f3
Autor:
Alain C. Vaucher, Federico Zipoli, Joppe Geluykens, Vishnu H. Nair, Philippe Schwaller, Teodoro Laino
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
Extracting experimental operations for chemical synthesis from procedures reported in prose is a tedious task. Here the authors develop a deep-learning model based on the transformer architecture to translate experimental procedures from the field of
Externí odkaz:
https://doaj.org/article/c608946fef074490b3844156d983475b
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