SSnet

Secondary Structure based End-to-End Learning model for Protein-Ligand Interaction Prediction. Abstract Computational prediction of bioactivity has become a critical aspect of modern drug discovery as it mitigates the cost, time, and resources required to find and screen new compounds. Deep Neural Networks (DNN) have recently shown excellent performance in modeling Protein-Ligand Interaction (PLI). However, DNNs are only effective when physically sound descriptions of ligands and proteins are fed into the network for further processing. Furthermore, previous research has not incorporated the secondary structure of the protein in a meaningful manner. In this work, we utilize secondary structure information of the protein which is extracted as the curvature and torsion of the backbone of protein to predict PLI. https://github.com/ekraka/SSnet