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Machine Learning
Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing
In recent years, there has been widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in various additive manufacturing (AM) techniques. These ML models excel at recognizing complex patterns from extensive, well-curated datasets,…
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Towards safer and efficient formulations: Machine learning approaches to predict drug-excipient…
Abstract
Predicting drug-excipient compatibility is a critical aspect of pharmaceutical formulation design. In this study, we introduced an innovative approach that leverages machine learning techniques to improve the accuracy of drug-excipient compatibility predictions. Mol2vec and 2D molecular…
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Using AI/ML to predict blending performance and process sensitivity for Continuous Direct…
Utilising three artificial intelligence (AI)/machine learning (ML) tools, this study explores the prediction of fill level in inclined linear blenders at steady state by mapping a wide range of bulk powder characteristics to processing parameters. Predicting fill levels enables the calculation of…
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A dataset of formulation compositions for self-emulsifying drug delivery systems
Abstract
Self-emulsifying drug delivery systems (SEDDS) are a well-established formulation strategy for improving the oral bioavailability of poorly water-soluble drugs. Traditional development of these formulations relies heavily on empirical observation to assess drug and excipient compatibility,…
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Data-driven development of an oral lipid-based nanoparticle formulation of a hydrophobic drug
Due to its cost-effectiveness, convenience, and high patient adherence, oral drug administration normally remains the preferred approach. Yet, the effective delivery of hydrophobic drugs via the oral route is often hindered by their limited water solubility and first-pass metabolism. To mitigate…
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Enabling late-stage drug diversification by high-throughput experimentation with geometric deep…
Late-stage functionalization is an economical approach to optimize the properties of drug candidates. However, the chemical complexity of drug molecules often makes late-stage diversification challenging. To address this problem, a late-stage functionalization platform based on geometric deep…
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Optimizing environmental sustainability in pharmaceutical 3D printing through machine learning
Abstract
3D Printing (3DP) of pharmaceuticals could drastically transform the manufacturing of medicines and facilitate the widespread availability of personalised healthcare. However, with increasing awareness of the environmental damage of manufacturing, 3DP must be eco-friendly, especially when…
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Revolutionizing Drug Formulation Development: The Increasing Impact of Machine Learning
Abstract
Over the past few years, the adoption of machine learning (ML) techniques has rapidly expanded across many fields of research including formulation science. At the same time, the use of lipid nanoparticles to enable the successful delivery of mRNA vaccines in the recent COVID-19 pandemic…
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Additive Manufacturing in Pharmaceuticals – Part 3
Additive Manufacturing in Pharmaceuticals
See the new book, edited by Dr. Subham Banerjee, Ph.D., Associate Professor in the Department of Pharmaceutics, National Institute of Pharmaceutical Education & Research (NIPER), Guwahati, Assam, India.
Description: This book presents the different…
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Additive Manufacturing in Pharmaceuticals – Part 2
Additive Manufacturing in Pharmaceuticals
See the new book, edited by Dr. Subham Banerjee, Ph.D., Associate Professor in the Department of Pharmaceutics, National Institute of Pharmaceutical Education & Research (NIPER), Guwahati, Assam, India.
Description: This book presents the different…
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