High throughput profiling of molecular shapes in crystals

Peter R. Spackman, Sajesh P. Thomas, Dylan Jayatilaka

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

Molecular shape is important in both crystallisation and supramolecular assembly, yet its role is not completely understood. We present a computationally efficient scheme to describe and classify the molecular shapes in crystals. The method involves rotation invariant description of Hirshfeld surfaces in terms of of spherical harmonic functions. Hirshfeld surfaces represent the boundaries of a molecule in the crystalline environment, and are widely used to visualise and interpret crystalline interactions. The spherical harmonic description of molecular shapes are compared and classified by means of principal component analysis and cluster analysis. When applied to a series of metals, the method results in a clear classification based on their lattice type. When applied to around 300 crystal structures comprising of series of substituted benzenes, naphthalenes and phenylbenzamide it shows the capacity to classify structures based on chemical scaffolds, chemical isosterism, and conformational similarity. The computational efficiency of the method is demonstrated with an application to over 14 thousand crystal structures. High throughput screening of molecular shapes and interaction surfaces in the Cambridge Structural Database (CSD) using this method has direct applications in drug discovery, supramolecular chemistry and materials design.
Original languageEnglish
Article number22204
JournalScientific Reports
Volume6
DOIs
Publication statusPublished - 24 Feb 2016

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