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Molecular modeling and simulation tools are used to study the structure of the molecules for the purpose of understanding and creating a new generation of technology that works on the nano-scale. The current techniques mainly focus on visualizing the molecule’s structure using many illustrative methods, while they leave the knowledge extraction load on the user that should be aware of many complex sciences. Developing a new innovative method in this perspective becomes crucial to support such fast development in such vital field of sciences. This paper represents a novel computer vision method for molecular modeling and simulation that gives the computer the ability to see and understand the structure of molecules just like the human eyes, and also the ability to analyze its structure without human intervention. The proposed approach is based on using the computer’s memory as a digital representation of the real 3D-physical scaled model of the molecule, and hence accommodates machine learning techniques for an automated analysis job. Moreover, a parallel processing approach has been adopted to speed up the whole process. The realistic case study of a glucose molecule reports the outstanding performance of the proposed approach to model and analyze its structure without human intervention. The proposed methodology makes the developing of an automated molecular expert system a one step away.

A Novel Computer Vision Methodology for Intelligent Molecular Modeling and Simulation