SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.

ABSTRACT: Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently shown excellent performance in PLI prediction. However, the performance is highly dependent on protein and ligand features utilized for the DNN model. Moreover, in current models, the deciphering of how protein features determine the underlying principles that govern PLI is not trivial. In this work, we developed a DNN framework named SSnet that utilizes secondary structure information of proteins extracted as the curvature and torsion of the protein backbone to predict PLI. We demonstrate the performance of SSnet by comparing against a variety of

SUBMITTER: Verma N 

PROVIDER: S-EPMC7869013 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature