Machine learning in drug development for central nervous system and neurological diseases predicting blood-brain-barrier permeability using ml models

The blood–brain barrier (BBB) is a protective structure that controls which molecules can move from the bloodstream into the brain. While it helps keep the brain’s environment stable, it also makes it difficult to develop drugs that need to reach the brain. Traditionally, scientists have used many cell experiments and animal tests to figure out whether a drug can cross the BBB, which is costly and time-consuming. To address this, researchers are increasingly using advanced computer methods that analyze existing data to predict BBB permeability. With the growth of machine learning and deep learning, many new models have been created to improve these predictions. Accurately predicting whether small molecules can cross the BBB is crucial for modern drug discovery. Machine learning models make these predictions faster and more accurate, helping scientists identify which compounds are most likely to reach the human brain. Predicting whether small molecules can pass through the blood–brain barrier (BBB) is important for discovering new drugs. Artificial intelligence tools, such as machine learning and large language models, can make this process faster and more accurate. The main goal of this research is to create new AI models and deep learning methods that can accurately predict if a molecule can cross the human BBB.