AIMC Topic: Protein Binding

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The accurate prediction and characterization of cancerlectin by a combined machine learning and GO analysis.

Briefings in bioinformatics Nov 5, 2021
Cancerlectins, lectins linked to tumor progression, have become the focus of cancer therapy research for their carbohydrate-binding specificity. However, the specific characterization for cancerlectins involved in tumor progression is still unclear. ...
Workflow Amino Acid Sequence Disease Susceptibility Gene Ontology Machine Learning Computational Biology Databases, Genetic Algorithms Structure-Activity Relationship Phylogeny Lectins Protein Binding Protein Interaction Domains and Motifs Neoplasms Peptides
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Forman persistent Ricci curvature (FPRC)-based machine learning models for protein-ligand binding affinity prediction.

Briefings in bioinformatics Nov 5, 2021
Artificial intelligence (AI) techniques have already been gradually applied to the entire drug design process, from target discovery, lead discovery, lead optimization and preclinical development to the final three phases of clinical trials. Currentl...
Protein Binding Machine Learning Proteins Databases, Protein Ligands
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The Impact of Crystallographic Data for the Development of Machine Learning Models to Predict Protein-Ligand Binding Affinity.

Current medicinal chemistry Oct 27, 2021
BACKGROUND: One of the main challenges in the early stages of drug discovery is the computational assessment of protein-ligand binding affinity. Machine learning techniques can contribute to predicting this type of interaction. We may apply these tec...
Ligands Protein Binding Proteins Machine Learning Databases, Protein Molecular Docking Simulation
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Protein-ligand binding affinity prediction model based on graph attention network.

Mathematical biosciences and engineering : MBE Oct 25, 2021
Estimating the binding affinity between proteins and drugs is very important in the application of structure-based drug design. Currently, applying machine learning to build the protein-ligand binding affinity prediction model, which is helpful to im...
Machine Learning Drug Design Protein Binding Proteins Ligands
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Integrating multi-scale neighbouring topologies and cross-modal similarities for drug-protein interaction prediction.

Briefings in bioinformatics Sep 2, 2021
MOTIVATION: Identifying the proteins that interact with drugs can reduce the cost and time of drug development. Existing computerized methods focus on integrating drug-related and protein-related data from multiple sources to predict candidate drug-t...
Models, Theoretical Drug Development Reproducibility of Results Humans Proteins Protein Binding Machine Learning Pharmaceutical Preparations Computational Biology Support Vector Machine Algorithms
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Persistent spectral hypergraph based machine learning (PSH-ML) for protein-ligand binding affinity prediction.

Briefings in bioinformatics Sep 2, 2021
Molecular descriptors are essential to not only quantitative structure activity/property relationship (QSAR/QSPR) models, but also machine learning based chemical and biological data analysis. In this paper, we propose persistent spectral hypergraph ...
Protein Binding Computational Biology Pharmaceutical Preparations Humans Algorithms Proteins Quantitative Structure-Activity Relationship Databases, Protein Machine Learning Ligands Binding, Competitive Drug Design Models, Molecular Drug Discovery Molecular Structure
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SAResNet: self-attention residual network for predicting DNA-protein binding.

Briefings in bioinformatics Sep 2, 2021
Knowledge of the specificity of DNA-protein binding is crucial for understanding the mechanisms of gene expression, regulation and gene therapy. In recent years, deep-learning-based methods for predicting DNA-protein binding from sequence data have a...
Computational Biology DNA Internet DNA-Binding Proteins Binding Sites Reproducibility of Results Deep Learning Protein Binding Humans Neural Networks, Computer Algorithms
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DeepDTAF: a deep learning method to predict protein-ligand binding affinity.

Briefings in bioinformatics Sep 2, 2021
Biomolecular recognition between ligand and protein plays an essential role in drug discovery and development. However, it is extremely time and resource consuming to determine the protein-ligand binding affinity by experiments. At present, many comp...
Data Accuracy Software Hydrogen Bonding Reproducibility of Results Proteins Binding Sites Models, Molecular Drug Discovery Amino Acid Sequence Protein Conformation, alpha-Helical Deep Learning Ligands Protein Binding
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NPI-GNN: Predicting ncRNA-protein interactions with deep graph neural networks.

Briefings in bioinformatics Sep 2, 2021
Noncoding RNAs (ncRNAs) play crucial roles in many biological processes. Experimental methods for identifying ncRNA-protein interactions (NPIs) are always costly and time-consuming. Many computational approaches have been developed as alternative way...
RNA, Untranslated Internet Benchmarking Deep Learning Datasets as Topic Protein Binding Proteins Software Humans Sensitivity and Specificity
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A spatial-temporal gated attention module for molecular property prediction based on molecular geometry.

Briefings in bioinformatics Sep 2, 2021
MOTIVATION: Geometry-based properties and characteristics of drug molecules play an important role in drug development for virtual screening in computational chemistry. The 3D characteristics of molecules largely determine the properties of the drug ...
Proteins Models, Molecular Molecular Structure Deep Learning Protein Binding Computational Biology Pharmaceutical Preparations Ligands Molecular Conformation Humans Neural Networks, Computer Reproducibility of Results Drug Discovery Software Algorithms
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