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@article{186882,
        author = {Zeba Masroor and Mohd Taqee Hussain and Syed Siddiq Ahmed and Muti Ur Rahman},
        title = {AI Driven Drug Discovery},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {2353-2360},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186882},
        abstract = {The process of drug discovery has traditionally been a time-consuming and costly endeavour, often taking 10-15 years and billions of dollars to bring a new drug to market. However, the advent of Artificial Intelligence (AI) in drug discovery is transforming the industry, offering faster, cheaper, and more accurate methods for identifying potential drug candidates, optimizing molecular structures, and predicting clinical outcomes. By leveraging machine learning, data analysis, and predictive modelling, AI significantly accelerates the discovery process, reducing the time it takes to bring effective treatments to market while improving success rates in clinical trials. AI-driven models can predict molecular properties, design compounds with specific characteristics, and identify drug-target interactions, making drug development more efficient. Despite these advantages, existing AI models often lack integration across all stages of drug discovery and struggle with balancing key factors such as potency, safety, and synthesizability. Moreover, the lack of explainability in some AI-driven predictions limits their practical adoption in real-world applications. This paper proposes an integrated, explainable AI framework for drug discovery, focusing on multi-objective optimization to improve potency, safety, and synthesizability, ensuring a faster, more reliable pathway to effective drug development.},
        keywords = {Artificial Intelligence (AI), Drug Discovery, Machine Learning, Multi-Objective Optimization, Explainable AI.},
        month = {November},
        }