Artificial Intelligence (AI), encompassing Machine Learning (ML) and Deep Learning (DL), is revolutionizing traditional drug discovery, a process traditionally characterized by long timelines (10 to 17 years) and high costs (up to 2.8 billion). AI enhances efficiency, accuracy, and success rates across the pharmaceutical pipeline, accelerating target identification, de novo drug design, virtual screening (VS), and clinical trial optimization. Key methodologies include Neural Networks (NNs), Graph Neural Networks (GNNs), and Natural Language Processing (NLP). Despite significant promise, widespread adoption is hindered by challenges relating to data quality, model interpretability ("black box" issues), and evolving ethical/regulatory frameworks. AI integration aims to usher in an era of personalized, cost-effective, and safer therapeutics.
Najiya Mulla* (Sun,) studied this question.