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Sensaas: Shape-based Alignment by Registration of Colored Point-based Surfaces

Abstract : sensaas is a tool developed for aligning and comparing molecular shapes and sub-shapes. Alignment is obtained by registration of 3D point-based representations of the van der Waals surface. The method uses local properties of the shape to identify the correspondence relationships between two point clouds containing up to several thousand colored (labeled) points. Our rigid-body superimposition method follows a two-stage approach. An initial alignment is obtained by matching pose-invariant local 3D descriptors, called FPFH, of the input point clouds. This stage provides a global superimposition of the molecular surfaces, without any knowledge of their initial pose in 3D space. This alignment is then refined by optimizing the matching of colored points. In our study, each point is colored according to its closest atom, which itself belongs to a user defined physico-chemical class. Finally, sensaas provides an alignment and evaluates the molecular similarity by using Tversky coefficients. To assess the efficiency of this approach, we tested its ability to reproduce the superimposition of X-ray structures of the benchmarking AstraZeneca (AZ) data set and, compared its results with those generated by the two shape-alignment approaches shaep and shafts. We also illustrated sub-matching properties of our method with respect to few substructures and bioisosteric fragments. The code is available upon request from the authors (demo version at
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Contributor : Frédéric Payan <>
Submitted on : Tuesday, June 23, 2020 - 4:25:02 PM
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Dominique Douguet, Frédéric Payan. Sensaas: Shape-based Alignment by Registration of Colored Point-based Surfaces. Molecular Informatics, Wiley-VCH, 2020, ⟨10.1002/minf.202000081⟩. ⟨hal-02879260⟩



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