Abstract
Recognition of molecular structural features is one of the most attractive fields in chemistry, especially when combining with machine learning techniques. Pattern recognition techniques are straightforward in recognizing graphic features, but little attention was given to recognize molecular structural features. In this work, we propose a new method taking advantage of pattern recognition techniques to analyze structural features and obtain novel chemical insights. Specifically, the cluster analysis is presented to recognize structural features, which provides an alternative to the most widely used root mean square deviation (RMSD) method and the recently proposed blob detection method. Based on this, the convex hull of the molecule is constructed. The convex hull of molecules is highly appealing in the sense that one can introduce established theorems and properties from other disciplines into chemistry. Novel molecular descriptors based on convex hulls can be defined and show encouraging results, especially in providing new insights in understanding non-covalent interactions, adsorption processes, etc.
The construction of local convex hull on the task of pattern recognition
