Einstein International Journal Organization(EIJO)

Abstract

Artificial intelligence (AI) is transforming drug discovery and pharmaceutical development by enabling data-driven decision-making, accelerating target identification, optimizing drug design, and improving clinical trial efficiency. Machine learning (ML), deep learning (DL), natural language processing (NLP), and generative AI models facilitate rapid analysis of complex biological datasets, prediction of pharmacological properties, and identification of novel therapeutic candidates. These technologies have the potential to reduce development timelines, costs, and failure rates while advancing precision medicine. However, significant methodological challenges remain, including data quality issues, model interpretability, regulatory uncertainties, ethical concerns, and integration with conventional experimental workflows. Despite these challenges, continued advancements in AI algorithms, multi-omics data integration, and collaborative frameworks between academia, industry, and regulators are expected to drive future innovation. This review critically examines the opportunities, methodological challenges, and future perspectives of AI-driven drug discovery, highlighting its transformative potential for modern pharmaceutical research.

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