2 citations · 3 across the 7 of their papers we have counts for
4 papers · 1 filter
Boltzmann priors for Implicit Transfer Operators
Juan Viguera Diez, Mathias Schreiner, Ola Engkvist +1
Accurate prediction of thermodynamic properties is essential in drug discovery and materials science. Molecular dynamics (MD) simulations provide a principled approach to this task…
Diversity-Aware Reinforcement Learning for de novo Drug Design
Hampus Gummesson Svensson, Christian Tyrchan, Ola Engkvist +1
Fine-tuning a pre-trained generative model has demonstrated good performance in generating promising drug molecules. The fine-tuning task is often formulated as a reinforcement lea…
PepINVENT: Generative peptide design beyond the natural amino acids
Gökçe Geylan, Jon Paul Janet, Alessandro Tibo +7
Peptides play a crucial role in the drug design and discovery whether as a therapeutic modality or a delivery agent. Non-natural amino acids (NNAAs) have been used to enhance the p…
Navigating the Maize: Cyclic and conditional computational graphs for molecular simulation
Thomas Löhr, Michele Assante, Michael Dodds +5
Many computational chemistry and molecular simulation workflows can be expressed as graphs. This abstraction is useful to modularize and potentially reuse existing components, as w…