AIMC Topic: Proteins

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Protein ligand binding site prediction using graph transformer neural network.

PloS one Aug 6, 2024
Ligand binding site prediction is a crucial initial step in structure-based drug discovery. Although several methods have been proposed previously, including those using geometry based and machine learning techniques, their accuracy is considered to ...
Binding Sites Algorithms Databases, Protein Protein Binding Drug Discovery Neural Networks, Computer Ligands Proteins Machine Learning
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Evolutionary Probability and Stacked Regressions Enable Data-Driven Protein Engineering with Minimized Experimental Effort.

Journal of chemical information and modeling Aug 1, 2024
Protein engineering through directed evolution and (semi)rational approaches is routinely applied to optimize protein properties for a broad range of applications in industry and academia. The multitude of possible variants, combined with limited scr...
Models, Molecular Probability Protein Engineering Machine Learning Proteins
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AI-Driven Deep Learning Techniques in Protein Structure Prediction.

International journal of molecular sciences Aug 1, 2024
Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established pr...
Models, Molecular Protein Conformation Computational Biology Proteins Artificial Intelligence Deep Learning
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Progress in the application of artificial intelligence in molecular generation models based on protein structure.

European journal of medicinal chemistry Jul 31, 2024
The molecular generation models based on protein structures represent a cutting-edge research direction in artificial intelligence-assisted drug discovery. This article aims to comprehensively summarize the research methods and developments by analyz...
Artificial Intelligence Drug Discovery Humans Proteins Protein Conformation Models, Molecular
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DGCPPISP: a PPI site prediction model based on dynamic graph convolutional network and two-stage transfer learning.

BMC bioinformatics Jul 31, 2024
BACKGROUND: Proteins play a pivotal role in the diverse array of biological processes, making the precise prediction of protein-protein interaction (PPI) sites critical to numerous disciplines including biology, medicine and pharmacy. While deep lear...
Databases, Protein Neural Networks, Computer Protein Interaction Mapping Computational Biology Machine Learning Proteins Deep Learning
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Biomimetic fusion: Platyper's dual vision for predicting protein-surface interactions.

Materials horizons Jul 29, 2024
Predicting protein binding with the material surface still remains a challenge. Here, a novel approach, platypus dual perception neural network (Platyper), was developed to describe the interactions in protein-surface systems involving bioceramics wi...
Neural Networks, Computer Ligands Surface Properties Molecular Dynamics Simulation Biomimetics Protein Binding Proteins
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Context-aware geometric deep learning for protein sequence design.

Nature communications Jul 25, 2024
Protein design and engineering are evolving at an unprecedented pace leveraging the advances in deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here, we introduce a deep learning appr...
Deep Learning Protein Conformation Models, Molecular Proteins Protein Engineering Amino Acid Sequence
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AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning.

Journal of molecular biology Jul 24, 2024
Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained ...
Databases, Protein Amino Acids Machine Learning Computational Biology Cluster Analysis Proteins
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Improving quantitative prediction of protein subcellular locations in fluorescence images through deep generative models.

Computers in biology and medicine Jul 23, 2024
Machine learning has been employed in recognizing protein localization at the subcellular level, which highly facilitates the protein function studies, especially for those multi-label proteins that localize in more than one organelle. However, exist...
Deep Learning Image Processing, Computer-Assisted Proteins Microscopy, Fluorescence Humans Machine Learning
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HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery.

Journal of chemical information and modeling Jul 22, 2024
We propose HydraScreen, a deep-learning framework for safe and robust accelerated drug discovery. HydraScreen utilizes a state-of-the-art 3D convolutional neural network designed for the effective representation of molecular structures and interactio...
Drug Discovery Deep Learning Neural Networks, Computer Drug Design Ligands Protein Binding Proteins
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