A Gridless Approach to Sampling and Classifying High-Dimensional Conformational Landscapes of Active Pharmaceutical Ingredients

We present a new method for calculating the high-dimensional conformational free energy landscapes of flexible drug-like molecules. Using Density Peaks Advanced's density estimator, the free energy associated with individual configurations sampled from an enhanced sampling simulation can be calculated in a gridless manner, thus enabling the mapping of conformational ensembles in dimensionalities computationally inaccessible to grid-based methods. Due to the physics-based configurational sampling, conformers can be characterized by the configurations corresponding to the density peaks. The gridless nature of this method enables this characterization in the full dimensionality of a flexible molecule's conformation space. This method can produce per-point free energy maps, which enable the study of conformational interchanges in a level of detail previously inaccessible. This method is demonstrated on molecules with 2, 4, and 11-dimensional conformational spaces and is presented alongside a set of consistency checks, which enable the quality of the high-dimensional results to be assessed.