Artificial intelligence: transforming drug discovery

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Artificial intelligence (AI) has improved medication research and development by shortening schedules, decreasing costs, and raising success rates. AI uses machine learning (ML), deep learning (DL), and natural language processing (NLP) to analyse large datasets and quickly identify pharmacological targets, forecast chemical efficacy, and optimise medication designs. It speeds up lead development by predicting pharmacokinetics, toxicity, and probable side effects. It also improves clinical trial designs through better patient recruitment and data analysis. Recent advances in protein structure prediction and generative molecular design have further expanded the potential of AI in pharmaceutical research. However, challenges include data quality, algorithmic bias, lack of interpretability, validation, reproducibility, and regulatory concerns remain. Overall, AI represents a powerful complementary technology that may significantly accelerate the discovery and development of safer and more effective medicines. This article highlights the role of artificial intelligence in drug discovery, its applications, challenges and future perspectives.