1 code implementation • 1 Oct 2022 • Daniel Probst
Potential societal and environmental effects such as the rapidly increasing resource use and the associated environmental impact, reproducibility issues, and exclusivity, the privatization of ML research leading to a public research brain-drain, a narrowing of the research effort caused by a focus on deep learning, and the introduction of biases through a lack of sociodemographic diversity in data and personnel caused by recent developments in machine learning are a current topic of discussion and scientific publications.
no code implementations • NeurIPS Workshop AI4Scien 2021 • Loïc Kwate Dassi, Matteo Manica, Daniel Probst, Philippe Schwaller, Yves Gaetan Nana Teukam, Teodoro Laino
Herein, we apply a Transformer architecture to a language representation of bio-catalyzed chemical reactions to learn the signal at the base of the substrate-active site atomic interactions.
no code implementations • 7 Jan 2021 • Opeoluwa Owoyele, Pinaki Pal, Alvaro Vidal Torreira, Daniel Probst, Matthew Shaxted, Michael Wilde, Peter Kelly Senecal
In the vicinity of the design optimum, the solution is refined by repeatedly running CFD simulations at the projected optimum and adding the newly obtained data to the training dataset.
1 code implementation • 9 Dec 2020 • Philippe Schwaller, Daniel Probst, Alain C. Vaucher, Vishnu H. Nair, David Kreutter, Teodoro Laino, Jean-Louis Reymond
Organic reactions are usually assigned to classes containing reactions with similar reagents and mechanisms.
no code implementations • 16 Aug 2019 • Daniel Probst, Jean-Louis Reymond
The chemical sciences are producing an unprecedented amount of large, high-dimensional data sets containing chemical structures and associated properties.