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Briefly, the algorithm starts at a random data point, calculates the distance to all points within the neighbourhood radius (ε) and, if at least a minimal number of points (MinPts) is encountered, it records the nearest neighbour distance (Reachability Distance) and the smallest radius that encircles MinPts objects (Core Distance). If less than MinPts points fall within ε, the point is considered as noise. The algorithm repeats the same procedure for the nearest neighbour point and proceeds iteratively until all data points have been visited, thereby generating an ordered list. Our specific choice of ε implies that none of the fragments is labelled 'noise' and each is included in one cluster at least. This choice allows to scan afterwards for clusters at any density. Distances d ij were calculated as the root mean square deviation in angular coordinates (aRMSD) between fragment pairs. Angle differences of ϕ 1 and ϕ 2 naturally fell into the value range [0,180], while for θ periodicity was removed to retain the value range [0,180]. The ordered list of Reachability Distances (RDs) can be drawn as a comprehensive nearest neighbour distance plot (called Reachability Plot).

For more details see our paper: structural_alphabet

Release v1.1.5 : DOI 10.5281/zenodo.46668