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@article{179038,
        author = {Neeraj Nikunj and Himanshu Chouhan and Pallavi Chand and Vikash Jhakhmola},
        title = {AI and Machine Learning in Drug Discovery: Current Applications, Challenges, and Future Directions in Pharmaceutical Research},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {5284-5293},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179038},
        abstract = {The integration of Artificial Intelligence (AI) in drug discovery has revolutionized pharmaceutical research, significantly reducing the time and cost associated with developing new drugs. AI, particularly Machine Learning (ML) and Deep Learning (DL) techniques, has enabled computer-aided drug discovery by leveraging vast biomedical datasets, advanced computing power, and cloud storage. DL models, such as artificial neural networks (ANNs), have enhanced predictive accuracy in key drug discovery processes, including drug–target interactions (DTIs), drug–drug similarity interactions (DDIs), drug sensitivity analysis, and side-effect predictions. Furthermore, AI-driven methodologies are accelerating the development of drugs for complex conditions, particularly central nervous system (CNS) disorders, where challenges such as blood–brain barrier permeability and high attrition rates persist. AI-powered techniques, including de novo drug design, virtual screening, and drug repurposing, have shown promise in tackling neurological diseases like Alzheimer's, Parkinson’s, and schizophrenia. Additionally, Explainable AI (XAI) is being incorporated to enhance transparency in drug discovery models, while digital twinning (DT) is emerging as a future research avenue. Open data sharing, model augmentation, and advancements in hybrid AI approaches will further strengthen AI’s role in pharmaceutical innovation, offering more efficient, cost-effective, and successful drug discovery strategies.},
        keywords = {Artificial Intelligence, Machine Learning, Deep Learning, Drug Discovery, Neural Networks, Drug–Target Interactions, Drug Repurposing, CNS Disorders, Explainable AI, Digital Twinning.},
        month = {May},
        }