Advanced AI Techniques for Predicting Drug Permeability Across Biological Barriers

The permeability of drugs across biological barriers is a key factor in determining their therapeutic efficacy and bioavailability. However, predicting how a drug interacts with these barriers, such as the blood-brain barrier (BBB), gastrointestinal epithelium, and skin, has historically been challenging. Traditional methods of permeability prediction, including in vitro assays and animal testing, are time-consuming, costly, and often lack accuracy when trying to simulate complex human physiology. Recently, artificial intelligence (AI) techniques, particularly machine learning (ML) and deep learning (DL), have shown tremendous promise in overcoming these challenges by analyzing large datasets and identifying patterns that traditional methods may overlook. AI models, such as Quantitative Structure-Activity Relationship (QSAR), neural networks, and support vector machines, have been successfully used to predict drug permeability based on molecular properties and interactions. These AI models not only offer faster and more cost-effective predictions but also enable a more accurate understanding of how drugs will behave in different biological environments. AI-driven predictions can optimize drug design by identifying promising candidates for permeability, improving the formulation of oral and transdermal drugs, and facilitating personalized medicine approaches. Despite challenges such as data quality and model interpretability, AI’s role in predicting drug permeability continues to grow, revolutionizing the drug development process. As data becomes more refined and AI techniques evolve, these models will play a critical role in creating safer, more effective drugs, ultimately improving treatment outcomes for a range of diseases.

کلیدواژه ها:

Drug permeability ، Artificial intelligence ، Machine learning ، Biological barriers