AIMC Topic: Models, Molecular

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GeoNet enables the accurate prediction of protein-ligand binding sites through interpretable geometric deep learning.

Structure (London, England : 1993) Nov 1, 2024
The identification of protein binding residues is essential for understanding their functions in vivo. However, it remains a computational challenge to accurately identify binding sites due to the lack of known residue binding patterns. Local residue...
Deep Learning Protein Binding Proteins DNA Computational Biology Models, Molecular Ligands RNA Binding Sites
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Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites.

Journal of chemical information and modeling Nov 1, 2024
In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. Traditionally, methods like X-ray crystallogra...
Protein Conformation Allosteric Site Models, Molecular Deep Learning Databases, Protein Ligands Proteins
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Using deep-learning predictions reveals a large number of register errors in PDB depositions.

IUCrJ Nov 1, 2024
The accuracy of the information in the Protein Data Bank (PDB) is of great importance for the myriad downstream applications that make use of protein structural information. Despite best efforts, the occasional introduction of errors is inevitable, e...
Models, Molecular Protein Conformation Proteins Databases, Protein Deep Learning
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Data and Molecular Fingerprint-Driven Machine Learning Approaches to Halogen Bonding.

Journal of chemical information and modeling Oct 29, 2024
The ability to predict the strength of halogen bonds and properties of halogen bond (XB) donors has significant utility for medicinal chemistry and materials science. XBs are typically calculated through expensive ab initio methods. Thus, the develop...
Models, Molecular Static Electricity Halogens Thermodynamics Machine Learning Density Functional Theory
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ANTIPASTI: Interpretable prediction of antibody binding affinity exploiting normal modes and deep learning.

Structure (London, England : 1993) Oct 25, 2024
The high binding affinity of antibodies toward their cognate targets is key to eliciting effective immune responses, as well as to the use of antibodies as research and therapeutic tools. Here, we propose ANTIPASTI, a convolutional neural network mod...
Deep Learning Protein Binding Models, Molecular Humans Neural Networks, Computer Binding Sites, Antibody Antigen-Antibody Complex Antibody Affinity Antibodies Antigens
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QSPR modeling to predict surface tension of psychoanaleptic drugs using the hybrid DA-SVR algorithm.

Journal of molecular graphics & modelling Oct 23, 2024
A robust Quantitative Structure-Property Relationship (QSPR) model was presented to predict the surface tension property of psychoanaleptic (psychostimulant and antidepressant) drugs. A dataset of 112 molecules was utilized, and three feature selecti...
Quantitative Structure-Activity Relationship Surface Tension Algorithms Models, Molecular Least-Squares Analysis Support Vector Machine
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Geometry-Augmented Molecular Representation Learning for Property Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Oct 9, 2024
Accurate molecular representation plays a crucial role in expediting the process of drug discovery. Graph neural networks (GNNs) have demonstrated robust capabilities in molecular representation learning, adept at capturing structural and spatial inf...
Computational Biology Neural Networks, Computer Machine Learning Models, Molecular Algorithms Drug Discovery
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AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network.

Interdisciplinary sciences, computational life sciences Oct 5, 2024
The structural stability of proteins is an important topic in various fields such as biotechnology, pharmaceuticals, and enzymology. Specifically, understanding the structural stability of protein is crucial for protein design. Artificial design, whi...
Neural Networks, Computer Protein Stability Artificial Intelligence Protein Conformation Algorithms Thermodynamics Proteins Models, Molecular
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Exploring the potential of structure-based deep learning approaches for T cell receptor design.

PLoS computational biology Sep 30, 2024
Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optim...
Protein Binding Protein Engineering Computational Biology Deep Learning Humans Models, Molecular Receptors, Antigen, T-Cell Protein Conformation
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Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence.

Molecules (Basel, Switzerland) Sep 29, 2024
The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computati...
Computational Biology Proteins Machine Learning Humans Models, Molecular Protein Engineering Drug Design Artificial Intelligence
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