239 citations · 574 across the 9 of their papers we have counts for
4 papers · 1 filter
Navigating protein landscapes with a machine-learned transferable coarse-grained model
Nicholas E. Charron, Felix Musil, Andrea Guljas +18
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. The development of a uni…
Machine Learning Small Molecule Properties in Drug Discovery
Nikolai Schapin, Maciej Majewski, Alejandro Varela +2
Machine learning (ML) is a promising approach for predicting small molecule properties in drug discovery. Here, we provide a comprehensive overview of various ML methods introduced…
Top-down machine learning of coarse-grained protein force-fields
Carles Navarro, Maciej Majewski, Gianni de Fabritiis
Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactions over extended timescales. Our me…
Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series
Francesc Sabanés Zariquiey, Adrià Pérez, Maciej Majewski +2
The accurate prediction of protein-ligand binding affinities is crucial for drug discovery. Alchemical free energy calculations have become a popular tool for this purpose. However…