Nanosciences
Odii Peter Egwuatu; Merit Oluchi Ori; Humphrey Sam Samuel; Francis-Dominic Makong Ekpan
Abstract
This review article explores the transformative impact of AI in the field of nanomedicine, specifically focusing on AI-enabled diagnostics and monitoring. Nanomedicine has emerged as a promising approach for improving medical imaging, drug delivery, diagnostics, and therapy, and AI has become a disruptive ...
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This review article explores the transformative impact of AI in the field of nanomedicine, specifically focusing on AI-enabled diagnostics and monitoring. Nanomedicine has emerged as a promising approach for improving medical imaging, drug delivery, diagnostics, and therapy, and AI has become a disruptive force that enhances the precision, efficiency, and personalization of healthcare solutions. We delve into the role of AI in designing and optimizing nanomaterials, drug delivery systems, and combinatorial nanomedicine administration. AI's potential to examine vast datasets, discover patterns and predict behaviour in biological systems is discussed. The paper also highlights the vital role of AI-driven nanosensors in the real-time monitoring of biomarkers within the human body. Interdisciplinary collaboration in healthcare is emphasized, as it is essential for addressing complex challenges and achieving global health goals. The article concludes by exploring how AI has revolutionized surgical planning, anatomical modelling, and virtual anatomy education in the context of nanomedicine. Overall, this review demonstrates the significant potential of AI-enabled diagnostics and monitoring in nanomedicine to revolutionize healthcare.
Basic Medicine
Merit Oluchi Ori; Francis-Dominic Makong Ekpan; Humphrey Sam Samuel; Odii Peter Egwuatu
Abstract
The integration of artificial intelligence (AI) and nanotechnology has revolutionized the field of nanomedicine. AI’s large-scale data processing and pattern recognition capabilities can enhance the design of nanotechnologies for diagnosis and therapy. This integration can address challenges in ...
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The integration of artificial intelligence (AI) and nanotechnology has revolutionized the field of nanomedicine. AI’s large-scale data processing and pattern recognition capabilities can enhance the design of nanotechnologies for diagnosis and therapy. This integration can address challenges in fabrication and targeted drug delivery for cancer therapy. AI’s rapid data mining and decision-making capabilities can lead to more innovative solutions. The convergence of biology, AI, and nanotechnology is fostering a scientific and technological revolution. Recent studies show that AI can improve the design of nanotechnologies for diagnostics and treatment by processing large datasets and recognizing complex patterns. AI is also used in nanomedicine design to optimize material properties based on interactions with target medications, biological fluids, immune systems, and cell membranes.
Chemistry
Humphrey Sam Samuel; Ugo Nweke-Maraizu; Emmanuel Edet Etim Etim
Abstract
Chalcogen bonding, a non-covalent interaction involving chalcogen atoms (e.g., sulfur, selenium, and tellurium), plays a crucial role in various chemical and biological processes. Understanding and characterizing chalcogen bonding interactions are essential for designing novel materials, medications, ...
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Chalcogen bonding, a non-covalent interaction involving chalcogen atoms (e.g., sulfur, selenium, and tellurium), plays a crucial role in various chemical and biological processes. Understanding and characterizing chalcogen bonding interactions are essential for designing novel materials, medications, and catalysts. In recent years, machine learning has emerged as a powerful tool for studying molecular interactions, including chalcogen bonding. This study provides an overview of the application of machine learning in characterizing chalcogen bonding. Experimental techniques, such as infrared (IR), nuclear magnetic resonance (NMR) spectroscopy, and X-ray crystallography, have been used to study chalcogen bonding. However, these methods often suffer from inherent experimental challenges. On the other hand, computational approaches, including quantum mechanics (QM) and molecular dynamics (MD) simulations, offer valuable insights into the electronic structure and energetics of chalcogen bonding. Nonetheless, they can be computationally demanding and may not fully encompass the diversity of chalcogen bonding interactions. Machine learning, with its ability to identify patterns and relationships in vast datasets, presents a promising alternative for characterizing chalcogen bonding. The study explains how machine learning algorithms, such as supervised and unsupervised learning, can be employed to classify and predict chalcogen-bonded complexes using neural network potentials to assess the persistence of chalcogen bonds in solution and ML models to predict two key solid-state synthesis conditions that must be specified for chalcogenide glasses. By integrating experimental data and computational results, machine learning models offer a holistic approach to understanding chalcogen bonding in various molecular systems. It emphasizes the integration of experimental and computational data as a means to maximize the accuracy and applicability of machine learning models and envisions a promising future for machine learning in characterizing chalcogen bonding interactions.
