Biomaterials in Their Role in Creating New Approaches for the Delivery of Drugs, Proteins, Nucleic Acids, and Mammalian Cells in Safety Pharmacology

Brown, L. R.

doi:10.1007/978-3-030-73317-9_25-1

Drug Discovery and Evaluation: Safety and Pharmacokinetic Assays 2022

DOI: 10.1007/978-3-030-73317-9_25-1

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Biomaterials in Their Role in Creating New Approaches for the Delivery of Drugs, Proteins, Nucleic Acids, and Mammalian Cells in Safety Pharmacology

L. R. Brown

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2024

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Cited by 2 publications

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Enhancing siRNA cancer therapy: Multifaceted strategies with lipid and polymer-based carrier systems

Dastgerdi,

Bayraktutan

et al. 2024

International Journal of Pharmaceutics

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Enhancing siRNA cancer therapy: Multifaceted strategies with lipid and polymer-based carrier systems

Dastgerdi,

Bayraktutan

et al. 2024

International Journal of Pharmaceutics

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Druggability of Pharmaceutical Compounds Using Lipinski Rules with Machine Learning

Nhlapho,

Nyathi,

Ngwenya

et al. 2024

sciphar

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In the field of pharmaceutical research, identifying promising pharmaceutical compounds is a critical challenge. The observance of Lipinski's Rule of Five (RO5) is a fundamental criterion, but evaluating many compounds manually requires significant resources and time. However, the integration of computational techniques in drug discovery in its early stages has significantly transformed the pharmaceutical industry, enabling further efficient screening and selection of possible drug candidates. Therefore, this study explores RO5 using algorithms of Machine Learning (ML), offering a comprehensive method to predict the druggability of pharmaceutical compounds. The study developed, evaluated, and validated the performance metrics of multiple supervised machine learning models. The best model was used to build an application that can predict and classify potential drug candidates. The findings revealed promising capabilities across all models for drug classification. Among all the explored models, Random Forest (RF), Extreme Gradient Boost (XGBoost), and Decision Tree (DT) classifiers demonstrated exceptional performance, achieving near-perfect accuracy of 99.94%, 99.81% and 99.87% respectively. This highlights the robustness of ensemble learning methods in classifying compounds based on RO5 adherence. The comparative analysis of these models underscores the importance of considering balanced accuracy, precision, F1-score, recall, and Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) score, interpretability, and computational efficiency when choosing between ML algorithms in drug discovery. The DrugCheckMaster application was subsequently developed using the most predictive model and is now available on Render (https://capstone-project-dc7w.onrender.com/).

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Biomaterials in Their Role in Creating New Approaches for the Delivery of Drugs, Proteins, Nucleic Acids, and Mammalian Cells in Safety Pharmacology

Cited by 2 publications

References 150 publications

Enhancing siRNA cancer therapy: Multifaceted strategies with lipid and polymer-based carrier systems

Enhancing siRNA cancer therapy: Multifaceted strategies with lipid and polymer-based carrier systems

Druggability of Pharmaceutical Compounds Using Lipinski Rules with Machine Learning

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