Sharif Digital Repository

Pharmaceutical active compounds existence in the water would cause serious ecological risks and human health-related adverse effects which turn to environmental concern, therefore several studies have done to remove pharmaceutical active compounds efficiently. Carbamazepine (CBZ), a widely used psychiatric drug, is one of the most frequently detected compounds in the surface water and groundwater which is studies in the current study. Recently, biocatalytic degradation using ligninolytic enzymes such as laccase provides a promising approach for their removal from water and wastewater. In this work, carbamazepine removal by immobilized laccase on modified membrane by multi wall carbon nanotube... 

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... 

Despite significant progress in cancer treatment, drug resistance remains a major challenge. Synergistic drug combinations offer a promising approach to overcome drug resistance and reduce side effects. Still, despite high-throughput testing technologies, existing drug combination databases suffer from biases and a lack of diversity in tested cancer cell lines, which challenges the prediction of drug response on novel cell targets. To address this critical need, we designed a two-level deep learning method that uses large-scale gene expression datasets to estimate the score and synergy of drug compounds on a wide variety of cancer cell lines. Our model includes an auto-encoder that train on...