239 citations · 574 across the 9 of their papers we have counts for
10 papers
PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks
Nikolai Schapin, Maciej Majewski, Mariona Torrens-Fontanals +1
Small molecule protonation is an important part of the preparation of small molecules for many types of computational chemistry protocols. For this, a correct estimation of the pKa…
ACEGEN: Reinforcement learning of generative chemical agents for drug discovery
Albert Bou, Morgan Thomas, Sebastian Dittert +10
In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. Howeve…
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…
Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
Maciej Majewski, Adrià Pérez, Philipp Thölke +7
A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships t…
TorchMD: A deep learning framework for molecular simulations
Stefan Doerr, Maciej Majewsk, Adrià Pérez +5
Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved…
Coarse Graining Molecular Dynamics with Graph Neural Networks
Brooke E. Husic, Nicholas E. Charron, Dominik Lemm +9
Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at atomic resolution. However, a coarse graining model…
SkeleDock: A Web Application for Scaffold Docking in PlayMolecule
Alejandro Varela-Rial, Maciej Majewski, Alberto Cuzzolin +2
SkeleDock is a scaffold docking algorithm which uses the structure of a protein-ligand complex as a template to model the binding mode of a chemically similar system. This algorith…