5 citations · 5 across the 4 of their papers we have counts for
5 papers
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…
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…
Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacy
Fabian P. Krüger, Johan Östman, 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…
Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels
Emma Svensson, Hannah Rosa Friesacher, Susanne Winiwarter +3
In the early stages of drug discovery, decisions regarding which experiments to pursue can be influenced by computational models. These decisions are critical due to the time-consu…
Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models
Hannah Rosa Friesacher, Ola Engkvist, Lewis Mervin +2
In the drug discovery process, where experiments can be costly and time-consuming, computational models that predict drug-target interactions are valuable tools to accelerate the d…