P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure

  title={P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure},
  author={Radoslav Kriv{\'a}k and David Hoksza},
  journal={Journal of Cheminformatics},
AbstractBackgroundLigand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although many methods have been published to date, only few of them are suitable for use in automated pipelines or for processing large datasets. These use cases require stability and speed, which disqualifies many of the recently… 

Exploring the computational methods for protein-ligand binding site prediction

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PrankWeb: a web server for ligand binding site prediction and visualization

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DeepSite: protein‐binding site predictor using 3D‐convolutional neural networks

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LIBRUS: combined machine learning and homology information for sequence-based ligand-binding residue prediction

A sequence-based method that combines homology-based transfer and direct prediction using machine learning and is slightly more discriminating than a support vector machine learner using profiles and predicted secondary structure is developed.

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