The Scope of AI in API/NCE Bioprospection

Le résumé fourni par la source

This chapter examines the revolutionary impact of artificial intelligence on the discovery and development of active pharmaceutical ingredients (APIs) and new chemical entities (NCEs) through bioprospection. It explores how machine learning algorithms are transforming traditional bioprospection methods by accelerating compound identification, predicting bioactivity, and optimizing lead candidates. The text analyzes current AI applications in natural product screening, including deep learning models for structure prediction and computer vision systems for high-throughput screening. Additionally, the significant limitations facing AI implementation were addressed, such as data quality concerns, algorithmic bias, insufficient training datasets for rare biological targets, challenge of modeling complex biological interactions, and the disconnect between in silico predictions and experimental efficacy. Case studies of successful AI-driven discoveries are presented alongside a critical assessment of where human expertise remains irreplaceable. The chapter concludes with emerging trends and ethical considerations that will shape the future integration of AI in pharmaceutical bioprospection.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.