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<p>Artificial intelligence (AI) has revolutionized drug discovery and development by accelerating timelines, reducing costs, and increasing success rates. AI leverages machine learning (ML), deep learning (DL), and natural language processing (NLP) to analyze vast datasets, enabling the rapid identification of drug targets, prediction of compound efficacy, and optimization of drug design. It accelerates lead discovery by predicting pharmacokinetics, toxicity, and potential side effects while also refining clinical trial designs through improved patient recruitment and data analysis. This review highlights the diverse benefits of AI in drug development, including enhanced efficiency, greater accuracy, and minimized risks. It also discussess critical challenges, such as data quality, model interpretability, and regulatory hurdles. Future advancements in AI-powered drug discovery will require a focus on improving data standardization, fostering transparency in AI model development, and strengthening collaboration between AI researchers and pharmaceutical experts. By addressing these challenges, AI holds the potential to transform the healthcare landscape by delivering safer, more effective, and more&#xa0;affordable medicines to patients.</p>

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Artificial intelligence in drug discovery and development: transforming challenges into opportunities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月2日

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  • Shashi Kant,
  • Deepika,
  • Saheli Roy

摘要

Artificial intelligence (AI) has revolutionized drug discovery and development by accelerating timelines, reducing costs, and increasing success rates. AI leverages machine learning (ML), deep learning (DL), and natural language processing (NLP) to analyze vast datasets, enabling the rapid identification of drug targets, prediction of compound efficacy, and optimization of drug design. It accelerates lead discovery by predicting pharmacokinetics, toxicity, and potential side effects while also refining clinical trial designs through improved patient recruitment and data analysis. This review highlights the diverse benefits of AI in drug development, including enhanced efficiency, greater accuracy, and minimized risks. It also discussess critical challenges, such as data quality, model interpretability, and regulatory hurdles. Future advancements in AI-powered drug discovery will require a focus on improving data standardization, fostering transparency in AI model development, and strengthening collaboration between AI researchers and pharmaceutical experts. By addressing these challenges, AI holds the potential to transform the healthcare landscape by delivering safer, more effective, and more affordable medicines to patients.