Automatic Device Selection for Molecular Visualization 

Metrics, Device selection, Molecular Docking, Molecular Conforma- tions, Molecular Trajectory, Molecular Symmetry. 

Metric is a quantifiable measure. The metrics are important to describe quantitatively, to evaluate and to compare different elements in Computer Science, like softwares and devices. We propose an automatic device selection using metrics to compare devices and to create a map device to help inexperienced users and suggest a different and possible more efficient map device for experienced users for an effective performance in molecular activities (MA). The MA perform analysis in molecular structures and are important to study the reactions among molecules and are used to discovery new drugs and materials for example. The MA can be performed using different devices. But, sometimes it is complicated to choose a good device to perform a MA, because the user may not know information about MA and interaction capabilities of devices. The metrics will classify devices analyzing interaction performance in components of visualization, specific needs of a MA, context information about devices not possible or not recommended for an en- vironment like CAVE and user’s preferences about experience and glad in relation to any device. The proposed approach uses metrics because are more impartial and objective. The metrics have a score to measure the interaction capabilities of devices. The measures of scores are time, quantity of some actions and distance. The first step of approach is obtained the score of metrics in generic test with users. The average result of tests will be the score of metrics. The second step is to apply the metrics with the scores obtained previously, to perform the automatic device selection and create an efficient map device. To evaluate the proposed approach we will compare the performance of user in a MA with map device chosen by user and map device proposed by our approach.