Computational Drug Discovery Tool leverages machine learning and QSAR analysis to accelerate the identification of potential Tuberculosis (TB) drugs targeting the DHFR enzyme. By simply inputting a canonical SMILES notation, researchers can predict the efficacy of a compound, reducing the reliance on expensive and time-consuming traditional drug discovery methods. This AI-driven approach aims to combat drug-resistant TB by streamlining the search for effective inhibitors, making TB treatment development faster and more efficient.