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

Corresponding author: Dr. Praveen Kumar Dasari Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Machine learning in drug development for central nervous system and neurological diseases predicting blood-brain-barrier permeability using ml models Praveen Kumar Dasari *, Krishna Durga Peddini, Nandhikola Harika, Rajaboina Lakshmi, Bellamkonda Vishnu Vardhan and Gundepuri Anil Mother Teresa Pharmacy College, Sathupally, Khammam, Telangana, India. GSC Biological and Pharmaceutical Sciences, 2025, 33(02), 379-383 Publication history: Received on 14October 2025; revised on 20 November 2025; accepted on 22 November 2025 Article DOI: https://doi.org/10.30574/gscbps.2025.33.2.0464 Abstract 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. Keywords: Blood Brain Barrier; Central Nervous System; Machine Learning; Permeability; Prediction 1. Introduction Neurological disorders are some of the hardest medical conditions to treat. They affect millions of people worldwide and create major challenges for individuals, families, and healthcare systems. Disorders like Alzheimer’s, Parkinson’s, epilepsy, multiple sclerosis, and several mental health conditions are complicated because they involve many different biological processes and symptoms, and current treatments are often limited.Developing new drugs for brain-related diseases is slow, costly, and has a high chance of failure. This is partly because scientists still don’t fully understand how these diseases work, and it’s difficult to predict how a drug will act inside the brain. One major obstacle is the blood– brain barrier (BBB), which prevents many drugs from entering the brain at all.Machine learning (ML) is helping researchers overcome these challenges. ML models can predict whether a drug can pass through the BBB more accurately, reducing the need for traditional lab and animal testing. Beyond that, ML can also help forecast how well a drug might work, its possible side effects, how it moves through the body, and how different patients might respond to treatment. Central Nervous System (CNS) disorders, such as Alzheimer's disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), epilepsy, and mental health conditions like schizophrenia, are becoming a growing challenge for healthcare systems around the world. Despite much research, developing new treatments for these diseases has been difficult. Traditional drug discovery methods often face problems, such as relying on animal models that don't always predict GSC Biological and Pharmaceutical Sciences, 2025, 33(02), 379-383 380 how a drug will work in humans, and the lack of reliable biomarkers to guide treatment development.Machine learning (ML), a type of artificial intelligence (AI), is increasingly being used in drug discovery and disease research. By analyzing large amounts of data and using advanced computer techniques, ML has the potential to speed up the drug development process, make predictions about how effective a drug might be, and reduce the chances of failure during clinical trials. In this review, we explore how ML change the Way for Drug developed. Figure 1 ML-enhanced application for neurological diseases diagnosis The BBB is a very selective barrier made of endothelial cells, pericytes, and astrocytes. It controls which substances can move from the blood into the brain. While this protects the brain, it also makes developing drugs for the CNS difficult. To enter the brain, drugs need certain chemical properties like the right level of lipophilicity, molecular weight, and ionization or they must use special transport systems to cross the BBB.Testing BBB permeability in the lab is expensive and time-consuming, so researchers increasingly use computer models to predict whether a compound can cross the barrier. These models analyze a molecule’s structure, chemical features, and other properties to estimate its ability to reach the brain. With the growth of large chemical databases and machine learning tools, these predictions have become more accurate. Objectives Accelerating Drug Discovery Finding new targets: ML can analyze large amounts of text and biological data to identify new protein targets and understand how diseases work. Designing new molecules: AI can create brand-new drug molecules quickly. Improving lead compounds: ML predicts how a drug behaves in the body—its PK/PD, toxicity, and effectiveness—so researchers can choose the best candidates. GSC Biological and Pharmaceutical Sciences, 2025, 33(02), 379-383 381 2. Enhancing clinical development 2.1. Better clinical trials ML helps design smarter clinical trials by reducing data variability and grouping patients more effectively. 2.2. Improving patient care Models can predict patient risk, suggest correct doses, and track medication adherence. 2.3. Biomarkers ML analyzes large datasets to identify better biomarkers and clinical endpoints. 3. Improving Diagnosis and Prognosis 3.1. Early diagnosis ML can study brain scans and genetic data to detect neurological diseases earlier and more accurately. 3.2. Predicting disease progression Models can forecast how a disease may develop and how well a patient might respond to treatment. 4. Addressing Challenges in Neurological Diseases 4.1. BBB penetration ML models help predict whether a drug can cross the blood-brain barrier, which is a major challenge in CNS drug development. 4.2. Brain complexity AI tools help scientists study the structure and function of the brain to better understand neurological disorders. 5. Future Applications and Challenges 5.1. Personalized medicine ML can analyze real-world patient data to create individualized treatments. 5.2. Model interpretability More explainable models are needed so clinicians can trust and use them. 5.3. Data integration Combining imaging, genetic, clinical, and molecular data remains a key challenge. 6. Discussions 6.1. Supervised Learning • These algorithms learn from labeled data to make predictions. • Used for: Predicting drug toxicity, drug effectiveness, BBB permeability, and drug–target interactions. 6.2. Unsupervised Learning: • These methods find hidden patterns in data without labels. • Used for: Groupinga similar molecules and identifying patterns in large omics datasets. GSC Biological and Pharmaceutical Sciences, 2025, 33(02), 379-383 382 6.3. Deep Learning • A type of ML that uses multi-layered neural networks to learn complex patterns. • Used for: Predicting protein structures, toxicity, and creating new molecules. 6.4. Generative Models (GANs, VAEs) • These models can create new data, such as novel chemical structures. • Used for: Designing new drug molecules with specific properties. 6.5. Reinforcement Learning Models learn through rewards and penalties to make better decisions over time. • Used for: Optimizing molecule design and finding the best inhibitors for target proteins. • Key Applications in Neurological Drug Development • BBB Permeability Prediction: ML helps predict which molecules can cross the blood–brain barrier, a major challenge in CNS drug development. • ADMET Prediction: ML can estimate how a drug behaves in the body, reducing development cost and time. • Drug Design: AI helps generate and refine new molecules tailored for neurological targets. • Diagnosis and Prognosis: ML analyzes genomic, imaging, and clinical data to improve early diagnosis and predict how diseases will progress. 7. Conclusion In conclusion, using machine learning (ML) to diagnose neurological and neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, and epilepsy offers great potential for improving medical research and clinical care. This review looked at different ML methodssuch as supervised and unsupervised learning, deep learning, and feature-selection techniquesand showed how they help build reliable diagnostic models. These technologies can improve accuracy in identifying diseases and predicting risks based on genetics or biomarkers, leading to better patient care and more personalized treatments. Future work should focus on making ML models easier to understand, solving issues like limited data through synthetic data generation, and testing models in diverse patient groups. It is also important to recognize that ML is designed to supportnot replaceclinicians. More studies are needed to show how combining ML tools with clinician expertise improves diagnosis, since many current studies evaluate ML systems alone. Strong collaboration between clinicians, computer scientists, and ethicists will be essential for successfully applying these technologies and ensuring they benefit all patients. 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