The Future of Precision Medicine and Artificial Intelligence in Drug Discovery: A Comprehensive Analysis of Instruments and Methods

Artificial Intelligence (AI) has revolutionised drug discovery and development by improving the speed, accuracy, and cost-effectiveness of the traditional method of finding novel therapeutic candidates. Computational and meta-analytical findings are used in this review to assess AI's effectiveness at different phases of drug discovery. It highlights important AI technologies like natural language processing (NLP), machine learning (ML), and deep learning (DL), which are being used more and more in precision medicine, virtual screening, pharmacological target identification, and repurposing. A quantitative synthesis of 12 independent studies using random-effects meta-analysis (DerSimonian-Laird method) revealed a pooled accuracy of 0.57 (95% CI: 0.38-0.76) in predicting drug-target interactions (DTIs). Furthermore, the study incorporates mathematical formulations for AI model performance evaluation, including Mean Squared Error (MSE), Receiver Operating Characteristic (ROC) area, and precision metrics. Despite challenges such as data heterogeneity, model interpretability, and ethical governance, the integration of explainable AI (XAI), multi-omics data fusion, and real-time analytics promises to restructure the future of pharmaceutical innovation.