The transmittable spread of viral coronavirus (SARS-CoV-2) has resulted in a significant rise in global mortality. Due to lack of effective treatment, our aim is to generate a highly potent active molecule that can bind with the protein structure of SARS-CoV-2. Different machine learning and deep learning approaches have been proposed for molecule generation; however, most of these approaches represent the drug molecule and protein structure in 1D sequence, ignoring the fact that molecules are by nature in 3D structure, and because of this many critical properties are lost. In this work, a framework is proposed that takes account of both tertiary and sequential representations of molecules and proteins using Gated Graph Neural Network (GGNN), Knowledge graph, and Early Fusion approach. The generated molecules from GGNN are screened using Knowledge Graph to reduce the search space by discarding the non-binding molecules before being fed into the Early Fusion model. Further, the binding affinity score of the generated molecule is predicted using the early fusion approach. Experimental result shows that our framework generates valid and unique molecules with high accuracy while preserving the chemical properties. The use of a knowledge graph claims that the entire generated dataset of molecules was reduced by roughly 96% while retaining more than 85% of good binding desirable molecules and the rejection of more than 99% of fruitless molecules. Additionally, the framework was tested with two of the SARS-CoV-2 viral proteins: RNA-dependent-RNA polymerase (RdRp) and 3C-like protease (3CLpro).