Artificial Intelligence in pharmaceutical sciences: Transforming drug discovery, formulation, and manufacturing
Authors/Creators
- 1. Libyan Association for Pharmacists
Description
Artificial intelligence, a major feature of machine learning and deep learning, is changing the way pharmaceutical research and development are done by speeding up drug discovery, formulation, stability prediction, and manufacturing processes. Artificial intelligence enables more rapid target identification, chemical space exploration, and de novo molecule design with fewer iterative experimental cycles, lead selection being optimised, and formulation behaviour and stability predicted. Machine learning and generative models are extensively used in early drug discovery for virtual screening, activity prediction, ADMET profiling, and multi, objective lead optimization resulting in development timelines being reduced drastically. In formulation and preformulation, artificial intelligence models are used to predict physicochemical properties, excipient interactions, and process effects leading to a dose form getting designed more efficiently even if it is complex. Stability prediction models are used to identify degradation pathways, moisture, induced changes, and biologics' aggregation thereby helping the formulation to be done early and the regulatory risk to be assessed. Artificial intelligence, assisted process analytical technology and digital twins in manufacturing lead to enhanced real, time monitoring, predictive control, and continuous manufacturing, thus quality and throughput are ensured. The main issues faced are data quality, model explainability, regulatory acceptance, and workforce readiness although the benefits have been demonstrated. The next steps will be the multimodal artificial intelligence integration, foundation models, or self-driving laboratories that will, without any doubt, speed up the process of pharmaceutical innovation even further. Together, artificial intelligence is no more a supplemental tool but rather the main driver of efficiency, quality, and predictability in pharmaceutical sciences.