GenBind — AI-Driven Molecular Generation and Protein Binding Explorer

Author(s)	Ms. Shravani Sanjay Deshmukh, Mr. Atharv Rajendra Bhaleghare, Mr. Vedant Shrinivas Bumrela, Dr. Deeplaxmi S Zingade
Country	India
Abstract	Traditional drug discovery is hindered by high costs, long development timelines, and low success rates. The integration of Artificial Intelligence (AI) in drug design has revolutionized the exploration of chemical space and accelerated the identification of novel therapeutics. This paper presents a survey and prototype implementation of GenBind — an AI-driven system for molecular generation and protein–ligand binding evaluation. The framework combines generative deep learning for de novo molecular design and computational docking for binding prediction. Using transformer-based molecule generators and AutoDock Vina docking, GenBind automates molecular synthesis, scoring, and ranking. The proposed architecture provides a foundation for scalable, data-driven drug discovery pipelines. Experimental evaluation demonstrates improved efficiency, high chemical validity, and potential for real-world applications in early-stage drug development.
Keywords	Artificial Intelligence, Drug Discovery, Molecular Generation, Protein Binding, Deep Learning, Docking, SMILES, Transformers, Computational Chemistry
Field	Biology > Genetics / Molecular
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-15
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.60690

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