Year: 2025
Type: article
Abstract: Conformational sampling is a key component of the workflow behind any machine learning force field. Existing methods for assessing the coverage of a conformational space can be ambiguous, reference-dependent, and qualitative due to their reliance on visual perception. In this work, we propose CHQuant, a protocol designed to quantify the extent of conformational space coverage. This approach is based on the observation that the movement of atoms defines point clouds that can be characterised by the volumes of the corresponding convex hulls. By applying our method to several small molecules and conformational sampling methods, we show that convex hull volumes behave according to chemical intuition at an atomic, functional group and molecular level. Therefore convex hull volumes are useful measures that can be used to compare different sampling methods or datasets. Among many applications, the CHQuant approach monitors the sampling achieved by any molecular dynamics sampler, and compares existing conformational databases of the same system.
Institution: University of Manchester
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Award: G8753896529
OpenAlex ID: W4417474837