Sharif Digital Repository

The aim of this study at the outset is synthesis of ligands with the ability to bridge formation between two groups i.e. C60 and Cobalt Schiff base complex (CoSALEN). Hence, four Schiff base ligands with pyridine electron donor moiety in one side, and such electron donor moiety pyridine, phenol and aniline in another side were synthesized and characterized by elemental analysis, 13C¬NMR, 1HNMR and IR Spectroscopy. In next step the interaction of these Schiff base ligands with CoSALEN complex and Fullerene C60, in the mixture of two solvents, i.e. toluene/dimethylsulfoxide and benzene/methanol was explored separately by UV-Visible Spectroscopy and their kinetic studies were also undertaken... 

Viability and proliferation of cells in cell culture mainly depends on i) cell culture medium, ii) cell-cell interaction, and iii) the supporting scaffold. Among these factors, and within recent years, surface and mechanical properties of the supporting ECM has attracted many researchers. Experiment results show that the animal cell adhesion is accelerated on stiffer scaffolds. Electrical charge of the scaffold is also a determinant term in the kinetics of adhesion and on a relatively charged scaffold, one can expect an increment in kinetics of adhesion front movement. This is due to the natural negative charges of the cell wall membrane. In this thesis, an analytical model is proposed to... 

Ethylene is considered as one of the key materials in the petrochemical industry and has a particular importance. Polymerized ethylene produces polyethylene, which is widely used in plastics and pipe-manufacturing industries. Ethylene is mainly produced by the processes of catalytic cracking or thermal cracking of heavier hydrocarbon compounds. In these processes, acetylene is produced as a by-product, which deactivates the Ziegler-Nata catalysts in the polymerization unit of ethylene. Therefore, there should be some considerations to reduce acetylene to its minimum value -around zero. For this purpose, the fixed bed catalytic reactor is used to hydrogenate acetylene to ethylene. Various... 

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... 

In the first project, we have described the preparation of quadruply linked naphthalimide compounds 3-6. These compounds showed weak and strong excimeric emissions in solid and solution states, respectively. The experimental and theoretical results showed that the adjacent naphthalimide rings were oriented in solution so that intramolecular interchromophoric interactions enhanced the quantum yield efficiencies. However, although the strong intramolecular π and solute-solvent interactions in solution were replaced by n→π*, inter- and intramolecular π-interactions in the solid state, the naphthalimide units were aligned so that the quantum yield efficiencies were diminished. Also, a rise in... 

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...