AIMC Topic: X-Ray Diffraction

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ML-Driven Pharmaceutical Cocrystal Technology: Advances in Screening, Property Prediction and Applications.

AAPS PharmSciTech
Recently, pharmaceutical cocrystal technology has garnered considerable global attention because of its innovativeness and environmental sustainability. This technology effectively enhances the bioavailability of poorly soluble drugs and optimizes th...

Machine learning-based fabrication of phytogenic NiO nanoparticles for anticancer activity in HepG2 Cell Culture.

Journal of materials science. Materials in medicine
Metal oxide nanomaterials play a central role in biomedical applications due to their unique physicochemical properties. In particular, various treatment methods such as drug delivery, hyperthermia therapy, radiation, and chemotherapy are used for th...

Integrated experimental, computational and machine learning approaches for the development of Apremilast-Aceclofenac coamorphous systems.

International journal of pharmaceutics
Understanding the molecular mechanisms of drug coamorphization remains a key challenge in solid-state pharmaceutics. This study presents a molecular level strategy for designing drug-drug coamorphous systems (CAMs) of apremilast (APR) and aceclofenac...

Machine Learning Tackles the Challenge of Powder X-ray Diffraction Indexing for All Crystal Systems.

Journal of chemical information and modeling
The indexing of powder X-ray diffraction (PXRD) in unknown structure determinations is a critical yet challenging step in crystallography, particularly for low-symmetry systems (e.g., monoclinic, triclinic) and/or large unit cell systems ( > 1000 Å)...

Comparative study on antibacterial activities and removal of iron ions from water using novel modified sand with silver through the hydrothermal technique.

Scientific reports
The hydrothermal-calcination technique was used to modify raw sand with silver (Ag) at different weight percentages: 2%, 5%, and 10% using silver nitrate. The raw and sand-coated Ag nanoparticle samples were analyzed using various techniques, includi...

Combining High-Throughput Screening and Machine Learning to Predict the Formation of Both Binary and Ternary Amorphous Solid Dispersion Formulations for Early Drug Discovery and Development.

Pharmaceutical research
OBJECTIVE: Amorphous solid dispersion (ASD) is widely utilized to enhance the solubility and bioavailability of water-insoluble drugs. However, conventional experimental approaches for ASD development are often resource-intensive and time-consuming. ...

Advanced machine learning-driven characterization of new natural cellulosic Lablab purpureus fibers through PCA and K-means clustering techniques.

International journal of biological macromolecules
The increasing demand for sustainable and eco-friendly materials has spurred significant interest in natural fibers as alternatives to synthetic reinforcements in composite applications. This study aims to explore the potential of Lablab purpureus fi...

Predicting RNA structure and dynamics with deep learning and solution scattering.

Biophysical journal
Advanced deep learning and statistical methods can predict structural models for RNA molecules. However, RNAs are flexible, and it remains difficult to describe their macromolecular conformations in solutions where varying conditions can induce confo...

Chemical characterization of hibiscus rosa-sinensis plant fibers facilitated through design of experiments and artificial neural network hybrid approach.

Scientific reports
The integration of natural fibers into Fiber Reinforced Polymers (FRPs) has emerged as a promising avenue for sustainable and high-performance composite materials. Natural fibers, derived from plants, offer notable advantages such as renewability, lo...