AI Medical Compendium Topic:
Protein Binding

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Evotuning protocols for Transformer-based variant effect prediction on multi-domain proteins.

Briefings in bioinformatics Nov 5, 2021
Accurate variant effect prediction has broad impacts on protein engineering. Recent machine learning approaches toward this end are based on representation learning, by which feature vectors are learned and generated from unlabeled sequences. However...
Protein Domains Amino Acid Sequence Protein Interaction Domains and Motifs Protein Binding Computational Biology Proteins Reproducibility of Results Deep Learning Neural Networks, Computer Algorithms
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Machine-learning scoring functions trained on complexes dissimilar to the test set already outperform classical counterparts on a blind benchmark.

Briefings in bioinformatics Nov 5, 2021
The superior performance of machine-learning scoring functions for docking has caused a series of debates on whether it is due to learning knowledge from training data that are similar in some sense to the test data. With a systematically revised met...
Molecular Conformation Models, Molecular Workflow Ligands Machine Learning Algorithms Databases, Factual Models, Statistical Regression Analysis Reproducibility of Results Protein Binding
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Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.

Briefings in bioinformatics Nov 5, 2021
Major histocompatibility complex (MHC) possesses important research value in the treatment of complex human diseases. A plethora of computational tools has been developed to predict MHC class I binders. Here, we comprehensively reviewed 27 up-to-date...
Neural Networks, Computer Machine Learning Deep Learning ROC Curve Protein Binding Computational Biology Software Databases, Factual Reproducibility of Results Amino Acid Sequence Binding Sites Carrier Proteins Web Browser Epitopes Histocompatibility Antigens Class I
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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. ...
Amino Acid Sequence Machine Learning Disease Susceptibility Gene Ontology Algorithms Databases, Genetic Neoplasms Structure-Activity Relationship Protein Binding Workflow Computational Biology Peptides Lectins Phylogeny Protein Interaction Domains and Motifs
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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 Databases, Protein Proteins Machine Learning 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...
Proteins Machine Learning Databases, Protein Molecular Docking Simulation Ligands Protein Binding
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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 Reproducibility of Results Humans Proteins Drug Development Protein Binding Pharmaceutical Preparations Machine Learning 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 Proteins Algorithms Humans Drug Design Machine Learning Databases, Protein Quantitative Structure-Activity Relationship Drug Discovery Models, Molecular Binding, Competitive Molecular Structure Pharmaceutical Preparations Ligands
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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 Reproducibility of Results Deep Learning DNA-Binding Proteins Protein Binding Binding Sites Humans Neural Networks, Computer Algorithms
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