Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Philippe Schwaller"'
Autor:
Jeff Guo, Philippe Schwaller
Publikováno v:
JACS Au, Vol 4, Iss 6, Pp 2160-2172 (2024)
Externí odkaz:
https://doaj.org/article/d9a7b55645d54dd599ac05ac34131678
Publikováno v:
Applied AI Letters, Vol 5, Iss 1, Pp n/a-n/a (2024)
Abstract We present Clipboard‐to‐SMILES Converter (C2SC), a macOS application that directly converts molecular structures from the clipboard. The app focuses on seamlessly converting screenshots of molecules into a desired molecular representatio
Externí odkaz:
https://doaj.org/article/34bf90078ba2438ea969324ce3af0bec
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
Publikováno v:
CHIMIA, Vol 77, Iss 1/2 (2023)
Reaction optimization is challenging and traditionally delegated to domain experts who iteratively propose increasingly optimal experiments. Problematically, the reaction landscape is complex and often requires hundreds of experiments to reach conver
Externí odkaz:
https://doaj.org/article/5dbb7ae8d0314a0789ca6669ae997c5e
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)
In organic chemistry, synthetic routes for new molecules are often specified in terms of reacting molecules only. The current work reports an artificial intelligence model to predict the full sequence of experimental operations for an arbitrary chemi
Externí odkaz:
https://doaj.org/article/dcccf88e9c3d4e90a2cd8b02fcfe56f3
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-8 (2020)
Organic reactions can readily be learned by deep learning models, however, stereochemistry is still a challenge. Here, the authors fine tune a general model using a small dataset, then predict and validate experimentally regio- and stereo-selectivity
Externí odkaz:
https://doaj.org/article/5d394ff268c64ce3b0faabdee491f557
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:
Amol Thakkar, Philippe Schwaller
Publikováno v:
CHIMIA, Vol 75, Iss 7-8 (2021)
Externí odkaz:
https://doaj.org/article/52b208e06932407c872f217e95fe0fc3
Autor:
Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A. Hunter, Costas Bekas, Alpha A. Lee
Publikováno v:
ACS Central Science, Vol 5, Iss 9, Pp 1572-1583 (2019)
Externí odkaz:
https://doaj.org/article/62107e3701c5494cb07ec9529b79d4b4