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

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 molecules with 4-, 7-, and 11-dimensional torsional angle spaces. As a key application, we investigate the solvent-dependent conformational preferences of bicalutamide in vacuum, chloroform, and DMSO. The predicted global minima reflect the known solvent-induced conformational shift between open and closed forms, in agreement with NMR and crystallographic data. These results demonstrate that our workflow provides a scalable route to high-dimensional conformational free energy landscapes, with direct relevance for polymorphism, solvation, and drug design.