• Surveyed literature on existing techniques of molecular generation using Transformers, VAE, MassGenie, Contrastive learning & MOSSES
• Analyzed the effects of change in the learning rate, batch size, scalability, and embedding lengths of SMILES representation in the ZINC dataset
• Obtained accuracy of 93% along with studying the experimented effect of Contrastive Learning on VAE models for robust performance

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