AIMC Topic: Crystallization
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MlCOFSyn: A Machine Learning Framework To Facilitate the Synthesis of 2D Covalent Organic Frameworks.
Journal of chemical information and modeling
Jun 5, 2025
Two-dimensional covalent organic frameworks (2D COFs) have been historically synthesized empirically, often resulting in uncontrolled crystallization and inferior crystal sizes, which limit their performance in various applications. Recently, crystal...
Analysis of drug crystallization by evaluation of pharmaceutical solubility in various solvents by optimization of artificial intelligence models.
Scientific reports
Jun 4, 2025
For analysis of crystallization, the solubility of drug in solvents should be correlated to input parameters. In this investigation, the solubility of salicylic acid as drug model in a variety of solvents is predicted through the utilization of multi...
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
Apr 3, 2025
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. ...
Automation of protein crystallization scaleup via Opentrons-2 liquid handling.
SLAS technology
Mar 16, 2025
In this study we present an approach for optimizing protein crystallization trials at the multi-microliter scale utilizing the Opentrons-2 liquid handling robot. Our research demonstrates the robot's capability to automate 24-well sitting drop protei...
Machine learning insights into calcium phosphate nucleation and aggregation.
Acta biomaterialia
Feb 18, 2025
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
Jan 17, 2025
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
Jul 15, 2024
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
Jun 11, 2024
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
Jan 1, 2024
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
Sep 7, 2023
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...
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