Sharif Digital Repository

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... 

A drug can be defined as a substance made to prevent disease, cure a specific symptom, relieve pain, and reduce anomalies in the body. The process of drug designing is so laborious, complex, costly, and time-consuming that chance of failure during the lab experiment stages is high. These challenges have persuaded researchers to find new usage for existing drugs, referred to as drug repurposing, with the main advantage of reducing cost, risk, and time. To this aim, computational methods have been applied to discover hidden pharmaceutical capabilities of drugs in terms of predicting whether a particular drug can interact with a particular protein.Graph Neural Networks (GNNs) have recently...