AIMC Topic: Proteins

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Improving drug-target interaction prediction through dual-modality fusion with InteractNet.

Journal of bioinformatics and computational biology Nov 11, 2024
In the drug discovery process, accurate prediction of drug-target interactions is crucial to accelerate the development of new drugs. However, existing methods still face many challenges in dealing with complex biomolecular interactions. To this end,...
Deep Learning Protein Binding Binding Sites Humans Pharmaceutical Preparations Machine Learning Computational Biology Neural Networks, Computer Ligands Algorithms Proteins Drug Discovery Databases, Protein
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LGS-PPIS: A Local-Global Structural Information Aggregation Framework for Predicting Protein-Protein Interaction Sites.

Proteins Nov 9, 2024
Exploring protein-protein interaction sites (PPIS) is of significance to elucidating the intrinsic mechanisms of diverse biological processes. On this basis, recent studies have applied deep learning-based technologies to overcome the high cost of we...
Humans Algorithms Binding Sites Computational Biology Deep Learning Protein Binding Protein Interaction Mapping Proteins Databases, Protein Protein Interaction Domains and Motifs
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Deep-GB: A novel deep learning model for globular protein prediction using CNN-BiLSTM architecture and enhanced PSSM with trisection strategy.

IET systems biology Nov 8, 2024
Globular proteins (GPs) play vital roles in a wide range of biological processes, encompassing enzymatic catalysis and immune responses. Enzymes, among these globular proteins, facilitate biochemical reactions, while others, such as haemoglobin, cont...
Position-Specific Scoring Matrices Computational Biology Proteins Databases, Protein Neural Networks, Computer Deep Learning
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AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics.

Journal of chemical theory and computation Nov 8, 2024
All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-...
Proteins Neural Networks, Computer Molecular Dynamics Simulation Machine Learning Thermodynamics
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A deep learning approach for rational ligand generation with toxicity control via reactive building blocks.

Nature computational science Nov 8, 2024
Deep generative models are gaining attention in the field of de novo drug design. However, the rational design of ligand molecules for novel targets remains challenging, particularly in controlling the properties of the generated molecules. Here, ins...
Humans Neural Networks, Computer Ligands Deep Learning Proteins Algorithms Bayes Theorem Drug Design
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Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction.

Nature communications Nov 7, 2024
Structure-based machine learning algorithms have been utilized to predict the properties of protein-protein interaction (PPI) complexes, such as binding affinity, which is critical for understanding biological mechanisms and disease treatments. While...
Models, Molecular Protein Interaction Mapping Computational Biology Proteins Neural Networks, Computer Machine Learning Algorithms Protein Binding
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Interaction-Based Inductive Bias in Graph Neural Networks: Enhancing Protein-Ligand Binding Affinity Predictions From 3D Structures.

IEEE transactions on pattern analysis and machine intelligence Nov 6, 2024
Inductive bias in machine learning (ML) is the set of assumptions describing how a model makes predictions. Different ML-based methods for protein-ligand binding affinity (PLA) prediction have different inductive biases, leading to different levels o...
Neural Networks, Computer Machine Learning COVID-19 Humans Proteins Ligands Protein Conformation Algorithms SARS-CoV-2 Protein Binding
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Ab initio characterization of protein molecular dynamics with AIBMD.

Nature Nov 6, 2024
Biomolecular dynamics simulation is a fundamental technology for life sciences research, and its usefulness depends on its accuracy and efficiency. Classical molecular dynamics simulation is fast but lacks chemical accuracy. Quantum chemistry methods...
Artificial Intelligence Proteins Density Functional Theory Molecular Dynamics Simulation Machine Learning Peptides Time Factors Protein Unfolding Nuclear Magnetic Resonance, Biomolecular Thermodynamics Protein Folding Quantum Theory Protein Conformation
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TPGPred: A Mixed-Feature-Driven Approach for Identifying Thermophilic Proteins Based on GradientBoosting.

International journal of molecular sciences Nov 5, 2024
Thermophilic proteins maintain their stability and functionality under extreme high-temperature conditions, making them of significant importance in both fundamental biological research and biotechnological applications. In this study, we developed a...
Machine Learning Algorithms Databases, Protein ROC Curve Computational Biology Proteins
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All-Atom Protein Sequence Design Based on Geometric Deep Learning.

Angewandte Chemie (International ed. in English) Nov 4, 2024
Designing sequences for specific protein backbones is a key step in creating new functional proteins. Here, we introduce GeoSeqBuilder, a deep learning framework that integrates protein sequence generation with side chain conformation prediction to p...
Thioredoxins Deep Learning Proteins Models, Molecular Amino Acid Sequence Protein Conformation
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