부산대학교 도서관

In the recent past, with the improvement of high throughput technology, the availability of protein structural data has increased exponentially. All these structural data have to be correctly mapped to their functional attributes to decode their biological role. However, to perform the functional annotation of these structural entities, the essential move is to locate the ligand-binding site (LBS) information. Although many approaches have been proposed to locate the LBS, most have low performance in terms of predictive quality. In this proposed work, we are presenting a deep neural network-based approach, DeepLBS, which uses geometrical as well as pharmacophoric properties to quantify the ligand-binding site (LBS) with high accuracy. To determine the efficiency of our work, DeepLBS was compared with the most recently developed deep learning tools. The result demonstrated that DeepLBS outperformed the existing state of art tools in terms of predictive quality.