000			01408nam a22001577a 4500
082			_a610
100			_aRazzaq, Ayesha _9130672
245			_aDrug Discovery Using Generative AI / _cAyesha Razzaq
264			_aIslamabad : _bSMME- NUST; _c2025.
300			_a104p. _bSoft Copy _c30cm
500			_aThis study investigated application of the Conditional Variational Autoencoder (CVAE) for de novo molecular designing focusing on three targets : Cyclin-dependent kinase 2 (CDK2),Peroxisome Proliferator Activator Receptor-gamma (PPAR-gamma),Dipeptide peptidase 4 (DPP-4). SMILES-based molecular representations is coupled with the physicochemical properties such as molecular weight, logP, hydrogen bond donors/acceptors, TPSA, and rotatable bonds. CVAE is trained to encode the meaningful lower-dimensional latent space representation of compounds. The resulting molecules are also checked for drug-likeness(QED ,SA), novelty,uniqueness and other metrics i.e. binding affinity using computational screening pipelines. SMILES format of the structural outputs were converted to SDF and PDB files and docked against targets in PyRx and binding interactions are analyzed in Discovery Studio.
650			_aMS Biomedical Engineering (BME) _9119509
700			_aSupervisor : Dr. Khawaja Fahad Iqbal _9125661
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/55104
942			_2ddc _cTHE
999			_c614844 _d614844