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Crystallization

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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
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...

Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins.

PloS one
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...

Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction.

Briefings in bioinformatics
Structure-based virtual screenings (SBVSs) play an important role in drug discovery projects. However, it is still a challenge to accurately predict the binding affinity of an arbitrary molecule binds to a drug target and prioritize top ligands from ...

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...

Nanostructured block copolymer muscles.

Nature nanotechnology
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...

SADeepcry: a deep learning framework for protein crystallization propensity prediction using self-attention and auto-encoder networks.

Briefings in bioinformatics
The X-ray diffraction (XRD) technique based on crystallography is the main experimental method to analyze the three-dimensional structure of proteins. The production process of protein crystals on which the XRD technique relies has undergone multiple...

Binary salt structure classification with convolutional neural networks: Application to crystal nucleation and melting point calculations.

The Journal of chemical physics
Convolutional neural networks are constructed and validated for the crystal structure classification of simple binary salts such as the alkali halides. The inputs of the neural network classifiers are the local bond orientational order parameters of ...

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...

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...