AIMC Topic: Ligands

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Protein-ligand binding residue prediction enhancement through hybrid deep heterogeneous learning of sequence and structure data.

Bioinformatics (Oxford, England) May 1, 2020
MOTIVATION: Knowledge of protein-ligand binding residues is important for understanding the functions of proteins and their interaction mechanisms. From experimentally solved protein structures, how to accurately identify its potential binding sites ...
Machine Learning Algorithms Proteins Ligands Binding Sites Protein Binding Computational Biology
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BionoiNet: ligand-binding site classification with off-the-shelf deep neural network.

Bioinformatics (Oxford, England) May 1, 2020
MOTIVATION: Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects...
Ligands Binding Sites Proteins Molecular Structure Neural Networks, Computer
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Learning from the ligand: using ligand-based features to improve binding affinity prediction.

Bioinformatics (Oxford, England) Feb 1, 2020
MOTIVATION: Machine learning scoring functions for protein-ligand binding affinity prediction have been found to consistently outperform classical scoring functions. Structure-based scoring functions for universal affinity prediction typically use fe...
Proteins Ligands Machine Learning Protein Binding
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Identification of Key Features of CNS Drugs Based on SVM and Greedy Algorithm.

Current computer-aided drug design Jan 1, 2020
INTRODUCTION: The research and development of drugs, related to the central nervous system (CNS) diseases is a long and arduous process with high cost, long cycle and low success rate. Identification of key features based on available CNS drugs is of...
Algorithms Computer Simulation Humans Pharmaceutical Preparations Drug Evaluation, Preclinical Ligands Blood-Brain Barrier Support Vector Machine Central Nervous System Guanidines
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Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data.

Bioinformatics (Oxford, England) Oct 15, 2019
MOTIVATION: Studies have shown that the accuracy of random forest (RF)-based scoring functions (SFs), such as RF-Score-v3, increases with more training samples, whereas that of classical SFs, such as X-Score, does not. Nevertheless, the impact of the...
Ligands Machine Learning Protein Binding Proteins
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PrankWeb: a web server for ligand binding site prediction and visualization.

Nucleic acids research Jul 2, 2019
PrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art method for ligand binding site prediction. P2Rank is a template-free machine learning method based on the prediction of local chemical neighborhood ligandability cent...
Protein Conformation, beta-Strand Protein Interaction Domains and Motifs Protein Conformation, alpha-Helical Binding Sites Thermodynamics Humans Internet Benchmarking Ligands Datasets as Topic Amino Acid Sequence Protein Binding Proteins Machine Learning Software
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Development of a protein-ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions.

Bioinformatics (Oxford, England) Apr 15, 2019
MOTIVATION: Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of FPs, and the Extended Connectivity Fingerprint (ECFP) is one of the best-suited for general applications. Despite the ove...
Ligands Proteins Databases, Protein Machine Learning Protein Binding
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A generic deep convolutional neural network framework for prediction of receptor-ligand interactions-NetPhosPan: application to kinase phosphorylation prediction.

Bioinformatics (Oxford, England) Apr 1, 2019
MOTIVATION: Understanding the specificity of protein receptor-ligand interactions is pivotal for our comprehension of biological mechanisms and systems. Receptor protein families often have a certain level of sequence diversity that converges into fe...
Proteins Phosphorylation Ligands Protein Kinases Neural Networks, Computer
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Automatic recognition of ligands in electron density by machine learning.

Bioinformatics (Oxford, England) Feb 1, 2019
MOTIVATION: The correct identification of ligands in crystal structures of protein complexes is the cornerstone of structure-guided drug design. However, cognitive bias can sometimes mislead investigators into modeling fictitious compounds without so...
Algorithms Protein Binding Machine Learning Binding Sites Electrons Ligands
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LigVoxel: inpainting binding pockets using 3D-convolutional neural networks.

Bioinformatics (Oxford, England) Jan 15, 2019
MOTIVATION: Structure-based drug discovery methods exploit protein structural information to design small molecules binding to given protein pockets. This work proposes a purely data driven, structure-based approach for imaging ligands as spatial fie...
Binding Sites Computational Biology Proteins Ligands Protein Conformation Drug Discovery Protein Binding Neural Networks, Computer Software
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