Sampling High-Dimensional Conformational Free Energy Landscapes of Active Pharmaceutical Ingredients.

ABSTRACT: We present a gridless framework for computing high-dimensional conformational free energy surfaces (FES) of flexible molecules using enhanced sampling trajectories. By combining concurrent well-tempered metadynamics with Density Peaks Advanced (DPA) clustering, our approach bypasses the dimensionality limitations of conventional grid-based FES reconstruction. Free energies are assigned on a per-configuration basis via local density estimation and Zwanzig reweighting, allowing for a direct, resolution-independent mapping of the conformational ensemble. Conformers are identified as density peaks in torsional angle space, and convergence is assessed via systematic consistency metrics. We validate this approach by reproducing the paradigmatic FES of alanine dipeptide and extend it to explore m

SUBMITTER: Ferreira A 

PROVIDER: S-EPMC12746462 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature