2 papers
physics.comp-ph2025
Basic stability tests of machine learning potentials for molecular simulations in computational drug discovery
Kavindri Ranasinghe, Adam L. Baskerville, Geoffrey P. F. Wood +1
Neural network potentials trained on quantum-mechanical data can calculate molecular interactions with relatively high speed and accuracy. However, neural network potentials might…
physics.comp-ph2024
An evaluation of machine learning/molecular mechanics end-state corrections with mechanical embedding to calculate relative protein-ligand binding free energies
Johannes Karwounopoulos, Mateusz Bieniek, Zhiyi Wu +4
The development of machine-learning (ML) potentials offers significant accuracy improvements compared to molecular mechanics (MM) because of the inclusion of quantum-mechanical eff…