Numerical Calibration Framework for Molecular Dynamics: Establishing Fidelity Baselines via Conservative Integration
Abstract
Reliable estimation of kinetic parameters in molecular dynamics (MD) requires distinguishing physical phenomena from numerical artifacts. Standard MD workflows often mask integration errors through empirical damping, potentially obscuring rare configurational transitions. We introduce a calibration framework employing intentionally conservative numerical parameters—including reduced timesteps (0.10 fs) and attenuated intermolecular forces—to establish a numerical fidelity baseline. This approach isolates integration artifacts from force-field complexities, providing a reference against which production MD methods can be benchmarked. By demonstrating stable integration under maximally challenging conditions, we provide a methodology for validating the numerical foundations of kinetic inference in drug discovery applications.
Fields of Study & Subjects
Protein Structure and Dynamics
Machine Learning in Materials Science
Computational Drug Discovery Methods
Publication details
- Journal / source
- Preprints.org
- Publication date
- 6 January 2026
- Document type
- Preprint