AIMC Topic: Ligands

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Structure-based drug design with geometric deep learning.

Current opinion in structural biology Feb 24, 2023
Structure-based drug design uses three-dimensional geometric information of macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric deep learning, an emerging concept of neural-network-based machine learning, has be...
Drug Discovery Ligands Deep Learning Machine Learning Drug Design Neural Networks, Computer
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Geometric Interaction Graph Neural Network for Predicting Protein-Ligand Binding Affinities from 3D Structures (GIGN).

The journal of physical chemistry letters Feb 16, 2023
Predicting protein-ligand binding affinities (PLAs) is a core problem in drug discovery. Recent advances have shown great potential in applying machine learning (ML) for PLA prediction. However, most of them omit the 3D structures of complexes and ph...
Ligands Proteins Neural Networks, Computer Protein Binding Machine Learning
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scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs.

Cell systems Feb 13, 2023
We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, using LR pairs as inter-data corre...
Ligands Cell Communication Communication Neural Networks, Computer
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Assessing protein homology models with docking reproducibility.

Journal of molecular graphics & modelling Feb 11, 2023
Results of the recent Critical Assessment of Protein Structure (CASP) competitions demonstrate that protein backbones can be predicted with very high accuracy. In particular, the artificial intelligence methods of AlphaFold 2 from DeepMind were able ...
Reproducibility of Results Protein Binding Artificial Intelligence Protein Conformation Binding Sites Ligands Proteins
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GB-score: Minimally designed machine learning scoring function based on distance-weighted interatomic contact features.

Molecular informatics Feb 1, 2023
In recent years, thanks to advances in computer hardware and dataset availability, data-driven approaches (like machine learning) have become one of the essential parts of the drug design framework to accelerate drug discovery procedures. Constructin...
Machine Learning Databases, Protein Ligands Proteins Protein Binding
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Deep-Learning-Enhanced Diffusion Imaging Assay for Resolving Local-Density Effects on Membrane Receptors.

Analytical chemistry Jan 30, 2023
G-protein-coupled receptor (GPCR) density at the cell surface is thought to regulate receptor function. Spatially resolved measurements of local-density effects on GPCRs are needed but technically limited by density heterogeneity and mobility of memb...
Signal Transduction Ligands Drug Inverse Agonism Receptors, G-Protein-Coupled Deep Learning Cell Membrane
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Persistent Path-Spectral (PPS) Based Machine Learning for Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling Jan 16, 2023
Molecular descriptors are essential to quantitative structure activity/property relationship (QSAR/QSPR) models and machine learning models. Here we propose persistent path-spectral (PPS), PPS-based molecular descriptors, and PPS-based machine learni...
Proteins Protein Binding Ligands Machine Learning Molecular Structure
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Improving Protein-Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge.

Biomolecules Jan 9, 2023
Elucidating protein-ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein-ligand interaction is traditionally tackled by molecular docking a...
Protein Conformation Cryoelectron Microscopy Ligands Deep Learning Proteins Molecular Docking Simulation
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MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein-Protein Docking Conformations.

Biomolecules Jan 6, 2023
Protein-protein interactions play a ubiquitous role in biological function. Knowledge of the three-dimensional (3D) structures of the complexes they form is essential for understanding the structural basis of those interactions and how they orchestra...
Machine Learning Proteins Protein Binding Protein Conformation Ligands
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3D Conformational Generative Models for Biological Structures Using Graph Information-Embedded Relative Coordinates.

Molecules (Basel, Switzerland) Dec 31, 2022
Developing molecular generative models for directly generating 3D conformation has recently become a hot research area. Here, an autoencoder based generative model was proposed for molecular conformation generation. A unique feature of our method is ...
Protein Conformation Models, Molecular Ligands Neural Networks, Computer
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