Sharif Digital Repository

Protein-ligand binding affinity is extremely important for finding new candidates in drug discovery and computational biochemistry. One of the physical characteristics for protein ligand interactions has been dissociation constant (KD) which can be obtain experimentally. However, there have been tremendous efforts to predict KD using modeling and computational approaches for protein-ligand interactions. In this project, we have exploited Convolutional Neural Network (CNN) model based on KDeep design, PDBBind version 2016 refined set training data, and examining it with KDeep core set test data. In order to modify KDeep,instead of 24 rotations (0, 90, 180 and 270 degrees in selection of two... 

Drug-target binding affinity prediction is one of the most important and vital part of drug discovery. The computational methods to predict binfing affinity is a standing challenge in drug discovery. State-of-the-art models are usually based on supervised machine learning with known label information. It is expensive and time-consuming to collect labeled data. This thesis proposes a semi-supervised model based on convolutional GAN (Generative adversarial networks). The model consists of two Gans and Two CNN blocks for feature extraction and fully connected layers for prediction. Gan can learn protein and drug features from unlabeled data. We evaluate the performance of our method using four... 

The Major Histocompatibility Complex (MHC) binds to the derived peptides from pathogens to present them to killer T cells on the cell surface. Developing computational methods for accurate, fast, and explainable peptide-MHC binding prediction can facilitate immunotherapies and vaccine development. Various deep learning-based methods rely on feature extraction from the peptide and MHC sequences separately and ignore their valuable binding information. This paper develops a capsule neural network-based method to efficiently capture and model the peptide-MHC complex features to predict the peptide- MHC class I binding. Various evaluations over multiple datasets using popular performance metrics...