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pro vyhledávání: '"van Deursen Ruud"'
Hyperparameter optimization is very frequently employed in machine learning. However, an optimization of a large space of parameters could result in overfitting of models. In recent studies on solubility prediction the authors collected seven thermod
Externí odkaz:
http://arxiv.org/abs/2407.20786
In the present paper we evaluated efficiency of the recent Transformer-CNN models to predict target properties based on the augmented stereochemical SMILES. We selected a well-known Cliff activity dataset as well as a Dipole moment dataset and compar
Externí odkaz:
http://arxiv.org/abs/2010.01027
We investigated the effect of different training scenarios on predicting the (retro)synthesis of chemical compounds using a text-like representation of chemical reactions (SMILES) and Natural Language Processing neural network Transformer architectur
Externí odkaz:
http://arxiv.org/abs/2003.02804
Prediction of molecular properties, including physico-chemical properties, is a challenging task in chemistry. Herein we present a new state-of-the-art multitask prediction method based on existing graph neural network models. We have used different
Externí odkaz:
http://arxiv.org/abs/1910.13124
Autor:
van Deursen, Ruud, Godin, Guillaume
Graphs and networks are a key research tool for a variety of science fields, most notably chemistry, biology, engineering and social sciences. Modeling and generation of graphs with efficient sampling is a key challenge for graphs. In particular, the
Externí odkaz:
http://arxiv.org/abs/1909.11472
Recurrent neural networks have been widely used to generate millions of de novo molecules in a known chemical space. These deep generative models are typically setup with LSTM or GRU units and trained with canonical SMILEs. In this study, we introduc
Externí odkaz:
http://arxiv.org/abs/1909.04825
Autor:
Homeyer, Nadine, van Deursen, Ruud, Ochoa-Montaño, Bernardo, Heikamp, Kathrin, Ray, Peter, Zuccotto, Fabio, Blundell, Tom L., Gilbert, Ian H.
Publikováno v:
In Journal of Molecular Graphics and Modelling March 2020 95
Autor:
van Deursen, Ruud1 (AUTHOR) ruud.van.deursen@firmenich.com, Ertl, Peter2 (AUTHOR), Tetko, Igor V.3,4 (AUTHOR), Godin, Guillaume1 (AUTHOR) guillaume.godin@firmenich.com
Publikováno v:
Journal of Cheminformatics. 4/10/2020, Vol. 12 Issue 1, p1-14. 14p.
Akademický článek
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Publikováno v:
In Journal of Molecular Catalysis. B, Enzymatic 2004 31(4):159-163