Zobrazeno 1 - 10
of 459
pro vyhledávání: '"Tetko, Igor V."'
This study investigates the risks of exposing confidential chemical structures when machine learning models trained on these structures are made publicly available. We use membership inference attacks, a common method to assess privacy that is largel
Externí odkaz:
http://arxiv.org/abs/2410.16975
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
Publikováno v:
In European Journal of Pharmaceutical Sciences 1 January 2025 204
Publikováno v:
In SLAS Discovery March 2024 29(2)
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