AIMC Topic: Crystallization

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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...

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...

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 ...

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...

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...

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...

20 years of crystal hits: progress and promise in ultrahigh-throughput crystallization screening.

Acta crystallographica. Section D, Structural biology
Diffraction-based structural methods contribute a large fraction of the biomolecular structural models available, providing a critical understanding of macromolecular architecture. These methods require crystallization of the target molecule, which r...

Analysis and estimation of cross-flow heat exchanger fouling in phosphoric acid concentration plant using response surface methodology (RSM) and artificial neural network (ANN).

Scientific reports
The production of phosphoric acid by dehydrated process leads to the precipitation of unwanted insoluble salts promoting thus the crystallization fouling build-up on heat transfer surfaces of the exchangers. During the acid concentration operation, t...

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization.

Chemical reviews
Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebr...

Machine learning-based protein crystal detection for monitoring of crystallization processes enabled with large-scale synthetic data sets of photorealistic images.

Analytical and bioanalytical chemistry
Since preparative chromatography is a sustainability challenge due to large amounts of consumables used in downstream processing of biomolecules, protein crystallization offers a promising alternative as a purification method. While the limited cryst...