3 papers
physics.chem-ph2025
Machine-Learned Electrostatic Potentials for Accurate Hydration Free Energy Calculations
Mathias Hilfiker, Leonardo Medrano Sandonas, Alexandre Tkatchenko +2
Free energy calculations are widely used tools in computational chemistry, but their dependence on the assignment of partial charges during force field parametrization reduces thei…
cs.LG2025
Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models
Hannah Rosa Friesacher, Emma Svensson, Susanne Winiwarter +3
The estimation of uncertainties associated with predictions from quantitative structure-activity relationship (QSAR) models can accelerate the drug discovery process by identifying…
cs.CR2024
Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacy
Fabian P. Krüger, Johan Ãstman, Lewis Mervin +2
This study investigates the risks of exposing confidential chemical structures when machine learning models trained on these structures are made publicly available. We use membersh…