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Proteins

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[Research progress in mechanism models and artificial intelligence models for protein expression systems].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Proteins are the basic building blocks of life. Studying the protein expression mechanism is essential for understanding the cellular organization principles and the development of biotechnology. Protein expression, involving transcription, translati...

[Intelligent mining, engineering, and design of proteins].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Natural components serve the survival instincts of cells that are obtained through long-term evolution, while they often fail to meet the demands of engineered cells for efficiently performing biological functions in special industrial environments. ...

[Artificial intelligence-enhanced physics-based computational modeling technologies for proteins].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Computational modeling is an invaluable tool for mechanism analysis, directed engineering, and rational design of biological parts, metabolic networks, and even cellular systems. It can provide new technological solutions to address biological challe...

Drug-Target Affinity Prediction Based on Topological Enhanced Graph Neural Networks.

Journal of chemical information and modeling
Graph neural networks (GNNs) have achieved remarkable success in drug-target affinity (DTA) analysis, reducing the cost of drug development. Unlike traditional one-dimensional (1D) sequence-based methods, GNNs leverage graph structures to capture ric...

PLPTP: A Motif-based Interpretable Deep Learning Framework Based on Protein Language Models for Peptide Toxicity Prediction.

Journal of molecular biology
Peptide toxicity prediction holds significant importance in drug development and biotechnology, as accurately identifying toxic peptide sequences is crucial for designing safer peptide-based drugs. This study proposes a deep learning-based model for ...

Atomic context-conditioned protein sequence design using LigandMPNN.

Nature methods
Protein sequence design in the context of small molecules, nucleotides and metals is critical to enzyme and small-molecule binder and sensor design, but current state-of-the-art deep-learning-based sequence design methods are unable to model nonprote...

Fitting Atomic Structures into Cryo-EM Maps by Coupling Deep Learning-Enhanced Map Processing with Global-Local Optimization.

Journal of chemical information and modeling
With the breakthroughs in protein structure prediction technology, constructing atomic structures from cryo-electron microscopy (cryo-EM) density maps through structural fitting has become increasingly critical. However, the accuracy of the construct...

Persistent Directed Flag Laplacian (PDFL)-Based Machine Learning for Protein-Ligand Binding Affinity Prediction.

Journal of chemical theory and computation
Directionality in molecular and biomolecular networks plays an important role in the accurate representation of the complex, dynamic, and asymmetrical nature of interactions present in protein-ligand binding, signal transduction, and biological pathw...

Cracking the protein compartmentalization code with ProtGPS.

Trends in biochemical sciences
Cellular protein compartmentalization is essential for function, yet the mechanisms directing proteins to their correct destinations remain unclear. Recently, Kilgore, Chinn, Mikhael, and Mitnikov et al. introduced ProtGPS, an artificial intelligence...

Advancing Molecular Simulations: Merging Physical Models, Experiments, and AI to Tackle Multiscale Complexity.

The journal of physical chemistry letters
Proteins and protein complexes form adaptable networks that regulate essential biochemical pathways and define cell phenotypes through dynamic mechanisms and interactions. Advances in structural biology and molecular simulations have revealed how pro...