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Crystallization

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Combining machine learning and molecular simulations to predict the stability of amorphous drugs.

The Journal of chemical physics
Amorphous drugs represent an intriguing option to bypass the low solubility of many crystalline formulations of pharmaceuticals. The physical stability of the amorphous phase with respect to the crystal is crucial to bring amorphous formulations into...

In silico co-crystal design: Assessment of the latest advances.

Drug discovery today
Pharmaceutical co-crystals represent a growing class of crystal forms in the context of pharmaceutical science. They are attractive to pharmaceutical scientists because they significantly expand the number of crystal forms that exist for an active ph...

Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection.

Briefings in bioinformatics
Protein crystallization is crucial for biology, but the steps involved are complex and demanding in terms of external factors and internal structure. To save on experimental costs and time, the tendency of proteins to crystallize can be initially det...

Graph Neural Networks with Multi-features for Predicting Cocrystals using APIs and Coformers Interactions.

Current medicinal chemistry
INTRODUCTION: Active pharmaceutical ingredients (APIs) have gained direct pharmaceutical interest, along with their in vitro properties, and thus utilized as auxiliary solid dosage forms upon FDA guidance and approval on pharmaceutical cocrystals whe...

Deep learning applications in protein crystallography.

Acta crystallographica. Section A, Foundations and advances
Deep learning techniques can recognize complex patterns in noisy, multidimensional data. In recent years, researchers have started to explore the potential of deep learning in the field of structural biology, including protein crystallography. This f...

Cocrystal Prediction of Nifedipine Based on the Graph Neural Network and Molecular Electrostatic Potential Surface.

AAPS PharmSciTech
Nifedipine (NIF) is a dihydropyridine calcium channel blocker primarily used to treat conditions such as hypertension and angina. However, its low solubility and low bioavailability limit its effectiveness in clinical practice. Here, we developed a c...

Structure to Property: Chemical Element Embeddings for Predicting Electronic Properties of Crystals.

Journal of chemical information and modeling
We present a new general-purpose machine learning model that is able to predict a variety of crystal properties, including Fermi level energy and band gap, as well as spectral ones such as electronic densities of states. The model is based on atomic ...

Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization.

Scientific reports
This study investigates the use of machine learning models to predict solubility of rivaroxaban in binary solvents based on temperature (T), mass fraction (w), and solvent type. Using a dataset with over 250 data points and including solvents encoded...

Machine learning insights into calcium phosphate nucleation and aggregation.

Acta biomaterialia
In this study, we utilized machine learning interatomic potentials (MLIPs) to investigate the nucleation mechanisms of calcium phosphate, a critical component of bone and teeth. Our analysis encompassed the process from pre-nucleation stage to the gr...

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. ...