AIMC Topic: Crystallization

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Machine learning-guided evolution of BMP-2 knuckle Epitope-Derived osteogenic peptides to target BMP receptor II.

Journal of drug targeting May 1, 2020
Bone morphogenetic protein-2 (BMP-2) is a key regulator of bone formation, growth and regeneration, which contains a conformational wrist epitope and a linear knuckle epitope that are functionally responsible for the protein by mediating its interact...
Humans Machine Learning Animals Osteogenesis Crystallization Peptides Bone Morphogenetic Protein Receptors Mice Epitopes
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Graph Convolutional Neural Networks as "General-Purpose" Property Predictors: The Universality and Limits of Applicability.

Journal of chemical information and modeling Jan 3, 2020
Nowadays the development of new functional materials/chemical compounds using machine learning (ML) techniques is a hot topic and includes several crucial steps, one of which is the choice of chemical structure representation. The classical approach ...
Density Functional Theory Models, Molecular Crystallization Neural Networks, Computer Machine Learning Algorithms Structure-Activity Relationship
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3D nanostructural characterisation of grain boundaries in atom probe data utilising machine learning methods.

PloS one Nov 18, 2019
Boosting is a family of supervised learning algorithm that convert a set of weak learners into a single strong one. It is popular in the field of object tracking, where its main purpose is to extract the position, motion, and trajectory from various ...
Machine Learning Algorithms Computer Simulation Imaging, Three-Dimensional Principal Component Analysis Nanostructures Crystallization
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Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks.

Journal of chemical information and modeling Nov 5, 2019
The surface energy of inorganic crystals is important in understanding experimentally relevant surface properties and designing materials for many applications. Predictive methods and data sets exist for surface energies of monometallic crystals. How...
Neural Networks, Computer Computer Simulation Density Functional Theory Alloys Titanium Thermodynamics Surface Properties Crystallization Models, Molecular Gold Models, Chemical
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Inexpensive robotic system for standard and fluorescent imaging of protein crystals.

Acta crystallographica. Section F, Structural biology communications Nov 5, 2019
Protein-crystallization imaging and classification is a labor-intensive process typically performed either by humans or by instruments that currently cost well over $100 000. This cost puts the use of crystallization-trial imaging outside the reach o...
Fluorescent Dyes Lasers Crystallization Printing, Three-Dimensional Proteins Robotics Costs and Cost Analysis Optical Imaging Muramidase Software Animals Chickens
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Analysis of crystallization phenomenon in Indian honey using molecular dynamics simulations and artificial neural network.

Food chemistry Jul 15, 2019
Molecular dynamics simulation was performed on sugar profile and moisture content-based mixture systems of six Indian honey samples. Comparative studies were performed to understand the interactive effects of fructose, glucose, sucrose, maltose and w...
Honey Fructose Crystallization Molecular Dynamics Simulation Sucrose Maltose India Glucose Neural Networks, Computer Water
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Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical Experiments.

Journal of chemical information and modeling May 22, 2019
Traditionally, chemists have relied on years of training and accumulated experience in order to discover new molecules. But the space of possible molecules is so vast that only a limited exploration with the traditional methods can be ever possible. ...
Crystallization Tungsten Compounds Machine Learning Inorganic Chemicals Humans Robotics Computational Chemistry Models, Chemical
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Combined molecular dynamics and neural network method for predicting protein antifreeze activity.

Proceedings of the National Academy of Sciences of the United States of America Dec 7, 2018
Antifreeze proteins (AFPs) are a diverse class of proteins that depress the kinetically observable freezing point of water. AFPs have been of scientific interest for decades, but the lack of an accurate model for predicting AFP activity has hindered ...
Antifreeze Proteins Humans Freezing Water Molecular Dynamics Simulation Protein Conformation Neural Networks, Computer Models, Theoretical Crystallization Thermodynamics Temperature Kinetics Animals
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General Method for the Identification of Crystal Faces Using Raman Spectroscopy Combined with Machine Learning and Application to the Epitaxial Growth of Acetaminophen.

Langmuir : the ACS journal of surfaces and colloids Aug 9, 2018
Crystal morphology is one of the key crystallographic characteristics that governs the macroscopic properties of crystalline materials. The identification of crystal faces, or face indexing, is an important technique that is used to get information r...
Machine Learning Mannitol Crystallography, X-Ray Algorithms Xylitol Galactose Spectrum Analysis, Raman Acetaminophen Crystallization X-Ray Diffraction
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Classification of crystallization outcomes using deep convolutional neural networks.

PloS one Jun 20, 2018
The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algori...
Crystallography, X-Ray Image Processing, Computer-Assisted Neural Networks, Computer Algorithms Datasets as Topic Crystallization
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