AIMC Topic: Crystallization

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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...
Pharmaceutical Preparations Crystallization Machine Learning Drug Discovery
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20 years of crystal hits: progress and promise in ultrahigh-throughput crystallization screening.

Acta crystallographica. Section D, Structural biology Feb 27, 2023
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...
Crystallization Models, Structural High-Throughput Screening Assays Biomedical Research Robotics
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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 Nov 28, 2022
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...
Hot Temperature Phosphoric Acids Neural Networks, Computer Crystallization
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Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization.

Chemical reviews Jun 27, 2022
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...
Crystallization Algorithms Automation Machine Learning Artificial Intelligence
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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 Jun 4, 2022
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...
Proteins Crystallization Image Processing, Computer-Assisted Neural Networks, Computer Machine Learning Algorithms
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Nanostructured block copolymer muscles.

Nature nanotechnology Jun 2, 2022
High-performance actuating materials are necessary for advances in robotics, prosthetics and smart clothing. Here we report a class of fibre actuators that combine solution-phase block copolymer self-assembly and strain-programmed crystallization. Th...
Nanostructures Crystallization Robotics Polymers Muscles
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Protein Crystallization Identification via Fuzzy Model on Linear Neighborhood Representation.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
X-ray crystallography is the most popular approach for analyzing protein 3D structure. However, the success rate of protein crystallization is very low (2-10 percent). To reduce the cost of time and resources, lots of computation-based methods are de...
Fuzzy Logic Proteins Crystallization Algorithms Computational Biology Support Vector Machine
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Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins.

PloS one Sep 2, 2021
Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simula...
Molecular Dynamics Simulation Protein Conformation, beta-Strand Crystallization Temperature Nuclear Energy Proteins Protein Structure, Tertiary Deep Learning Protein Conformation, alpha-Helical
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CrystalM: A Multi-View Fusion Approach for Protein Crystallization Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2021
Improving the accuracy of predicting protein crystallization is very important for protein crystallization projects, which is a critical step for the determination of protein structure by X-ray crystallography. At present, many machine learning metho...
Proteins Support Vector Machine Crystallization Sequence Analysis, Protein Computational Biology Models, Statistical Position-Specific Scoring Matrices
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A high-throughput system combining microfluidic hydrogel droplets with deep learning for screening the antisolvent-crystallization conditions of active pharmaceutical ingredients.

Lab on a chip May 18, 2020
Crystallization of active pharmaceutical ingredients (APIs) is a crucial process in the pharmaceutical industry due to its great impact in drug efficacy. However, conventional approaches for screening the optimal crystallization conditions of APIs ar...
Solvents Deep Learning Microfluidics Crystallization Hydrogels
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