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This 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.