AIMC Topic: Solubility
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Hierarchical Graph Attention Network with Positive and Negative Attentions for Improved Interpretability: ISA-PN.
Journal of chemical information and modeling
Dec 9, 2024
With the advancement of deep learning (DL) methods in chemistry and materials science, the interpretability of DL models has become a critical issue in elucidating quantitative (molecular) structure-property relationships. Although attention mechanis...
FormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction.
Molecular pharmaceutics
Dec 8, 2024
The Biopharmaceutics Classification System (BCS) has facilitated biowaivers and played a significant role in enhancing drug regulation and development efficiency. However, the productivity of measuring the key discriminative properties of BCS, solubi...
Novel active Trp- and Arg-rich antimicrobial peptides with high solubility and low red blood cell toxicity designed using machine learning tools.
International journal of antimicrobial agents
Dec 5, 2024
BACKGROUND: Given the rising number of multidrug-resistant (MDR) bacteria, there is a need to design synthetic antimicrobial peptides (AMPs) that are highly active, non-hemolytic, and highly soluble. Machine learning tools allow the straightforward i...
High-Speed Imaging-Based Particle Attribute Analysis of Spray-Dried Amorphous Solid Dispersions Using a Convolution Neural Network.
Molecular pharmaceutics
Dec 2, 2024
Spray drying is a well-established method for preparing amorphous solid dispersion (ASD) formulations to improve the oral bioavailability of poorly soluble drugs. In addition to the characterization of the amorphous phase, particle attributes of spra...
Machine Learning Models for Predicting Monoclonal Antibody Biophysical Properties from Molecular Dynamics Simulations and Deep Learning-Based Surface Descriptors.
Molecular pharmaceutics
Nov 28, 2024
Monoclonal antibodies (mAbs) have found extensive applications and development in treating various diseases. From the pharmaceutical industry's perspective, the journey from the design and development of mAbs to clinical testing and large-scale produ...
A non-linear modelling approach to predict the dissolution profile of extended-release tablets.
European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Nov 28, 2024
This study proposes a novel non-linear modelling approach to predict the dissolution profiles of extended-release tablets, by combining a full-factorial design, curve fitting to the dissolution profiles, and artificial neural networks (ANN), with lin...
Phosphate-solubilizing fungus (PSF) - mediated phosphorous solubilization and validation through Artificial intelligence computation.
World journal of microbiology & biotechnology
Nov 2, 2024
Phosphate-solubilizing fungus (PSF) strain alaromyces funiculosus was investigated for phosphorus solubilization, utilizing a range of pH levels and phosphate sources, followed by data confirmation through artificial intelligence modeling. T. funicul...
Machine learning driven bioequivalence risk assessment at an early stage of generic drug development.
European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
Oct 26, 2024
BACKGROUND: Bioequivalence risk assessment as an extension of quality risk management lacks examples of quantitative approaches to risk assessment at an early stage of generic drug development. The aim of our study was to develop a model-based approa...
Data-driven insights into the properties of liquisolid systems based on machine learning algorithms.
European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Oct 6, 2024
Liquisolid systems (LS) represent a formulation approach where liquid drug or its dispersion is transformed into a powder with good flowability and compactibility, leading to enhanced drug dissolution and bioavailability. Many research groups have fo...
Advancing pharmaceutical Intelligence via computationally Prognosticating the in-vitro parameters of fast disintegration tablets using Machine Learning models.
European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
Sep 19, 2024
The field of Machine Learning (ML) has garnered significant attention, particularly in healthcare for predicting disease severity. Recently, the pharmaceutical sector has also adopted ML techniques in various stages of drug development. Tablets are t...
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