AIMC Topic: Models, Molecular

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AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning.

Journal of chemical information and modeling Nov 24, 2024
Graphs are one of the most natural and powerful representations available for molecules; natural because they have an intuitive correspondence to skeletal formulas, the language used by chemists worldwide, and powerful, because they are highly expres...
Machine Learning Models, Molecular
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Accurate RNA 3D structure prediction using a language model-based deep learning approach.

Nature methods Nov 21, 2024
Accurate prediction of RNA three-dimensional (3D) structures remains an unsolved challenge. Determining RNA 3D structures is crucial for understanding their functions and informing RNA-targeting drug development and synthetic biology design. The stru...
Software RNA Humans Computational Biology Deep Learning Models, Molecular Nucleic Acid Conformation
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ProAffinity-GNN: A Novel Approach to Structure-Based Protein-Protein Binding Affinity Prediction via a Curated Data Set and Graph Neural Networks.

Journal of chemical information and modeling Nov 18, 2024
Protein-protein interactions (PPIs) are crucial for understanding biological processes and disease mechanisms, contributing significantly to advances in protein engineering and drug discovery. The accurate determination of binding affinities, essenti...
Models, Molecular Protein Conformation Protein Interaction Mapping Proteins Neural Networks, Computer Databases, Protein Protein Binding
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Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds.

PLoS computational biology Nov 18, 2024
Peptides are an emerging modality for developing therapeutics that can either agonize or antagonize cellular pathways associated with disease, yet peptides often suffer from poor chemical and physical stability, which limits their potential. However,...
Machine Learning Amino Acid Sequence Computational Biology Protein Folding Disulfides Models, Molecular Peptides Peptide Library
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A Divide-and-Conquer Approach to Nanoparticle Global Optimisation Using Machine Learning.

Journal of chemical information and modeling Nov 15, 2024
Global optimization of the structure of atomic nanoparticles is often hampered by the presence of many funnels on the potential energy surface. While broad funnels are readily encountered and easily exploited by the search, narrow funnels are more di...
Metal Nanoparticles Thermodynamics Nanoparticles Models, Molecular Machine Learning
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deepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction.

Biomolecules Nov 14, 2024
Coarse-grained models have provided researchers with greatly improved computational efficiency in modeling structures and dynamics of biomacromolecules, but, to be practically useful, they need fast and accurate conversion methods back to the all-ato...
Computational Biology Software Proteins Models, Molecular Neural Networks, Computer Deep Learning
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Machine learning-based prediction of bioactivity in HIV-1 protease: insights from electron density analysis.

Future medicinal chemistry Nov 12, 2024
To develop a model for predicting the biological activity of compounds targeting the HIV-1 protease and to establish factors influencing enzyme inhibition. Machine learning models were built based on a combination of Richard Bader's theory of Atoms ...
HIV Protease HIV Protease Inhibitors HIV-1 Ligands Hydrogen Bonding Humans Machine Learning Models, Molecular Electrons
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Deep learning for intrinsically disordered proteins: From improved predictions to deciphering conformational ensembles.

Current opinion in structural biology Nov 12, 2024
Intrinsically disordered proteins (IDPs) lack a stable three-dimensional structure under physiological conditions, challenging traditional structure-based prediction methods. This review explores how modern deep learning approaches, which have revolu...
Deep Learning Models, Molecular Humans Intrinsically Disordered Proteins Protein Conformation
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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 Algorithms Protein Binding Computational Biology Proteins Neural Networks, Computer Machine Learning
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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...
Deep Learning Proteins Protein Conformation Thioredoxins Models, Molecular Amino Acid Sequence
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