Binding Site Prediction

• AF2BIND: AF2BIND utilizes AlphaFold2 for predicting protein-ligand binding sites.

  

  

• BindWeb: a web server for ligand binding residue and pocket prediction from protein structures (online)

  

  

• BiteNet: large-scale detection of protein binding sites (online)

  

  

• BiteNet: Spatiotemporal identification of druggable binding sites using deep learning (standalone)
  

  

• COACH-D: COACH-D: improved protein–ligand binding sites prediction with refined ligand-binding poses through molecular docking

  

  

• CRAFT: cavity prediction tool based on flow transfer algorithm (standalone), 2024.
  

  

  

• CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning (PPI and regular binding sites) (online)

  

  

• Caviar: Protein cavity identification and automatic subpocket decomposition (standalone)
  

  

• CavityPlus: A web server for protein cavity detection with pharmacophore modeling, allosteric site identification, and covalent ligand binding ability prediction.
  

  

• ConCavity: prediction from protein sequence and structure (geometry-based) (online)

  

  

• DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks (standalone)
  

  

• DeepSite:

  

• DeepSurf: a surface-based deep learning approach for the prediction of ligand binding sites on proteins (standalone)
  

  

• DogSiteScorer: This proteins.plus platform offers DogSiteScorer for predicting and scoring protein-ligand binding sites.

  

  

• ESSA: Essential site scanning analysis: A new approach for detecting sites that modulate the dispersion of protein global motions (Essential sites are defined as residues that would significantly alter the protein’s global dynamics if bound to a ligand) (standalone)

  

• EquiPocket: E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
  

  

• FPocketWeb: FpocketWeb is a browser app for identifying pockets on protein surfaces where small-molecule ligands might bind, running calculations locally on the user's computer.
  

  

• FrustraPocket: A protein–ligand binding site predictor using energetic local frustration (standalone)
  

  

• GENEOnet: XAI approach to protein pocket detection

  

  

• GRaSP-py: a graph-based residue neighborhood strategy to predict binding sites (standalone)
  

  

  

• GeoNet: graph neural network-based predictor for identifying nucleic acid- and protein-binding residues on protein structures
  

  

• GetCleft: The detection of cavities, both internal or surface-exposed (standalone)
  

• Get_Cleft:
  

• GrASP: GrASP (Graph Attention Site Prediction) identifies druggable binding sites using graph neural networks with attention.
  

  

• Graphsite-classifier: deep graph neural network to classify ligand-binding sites on proteins (standalone)
  

  

• IF-SitePred: IF-SitePred is a method for predicting ligand-binding sites on protein structures. It first generates an embedding for each residue of the protein using the ESM-IF1 model, then performs point cloud clustering to identify binding site centres.
  

  

• Kalasanty: Improving detection of protein-ligand binding sites with 3D segmentation (standalone)
  

  

• LVPocket: 3D global-local information to protein binding pockets prediction
  

  

• P2rank: P2Rank is a machine learning-based tool for predicting ligand-binding sites from protein structures, capable of handling various structure formats including AlphaFold models.
  

  

• POCASA: geometry-based, pockets and cavities, volume... (online)

  

  

• PUResNet: Predicting protein-ligand binding sites using deep convolutional neural network (binding pocket) (standalone)
  

  

  

• PUResNetV2.0: state-of-the-art deep learning model designed to predict ligand binding sites in protein structures.
  

  

  

• PocketAnalyzerPCA: Identify cavities and crevices in proteins (standalone)

• PocketDruggability: A model that predicts the “attainable binding affinity” for a given binding pocket on a protein; this model relies on 13 physiochemical and structural features calculated using the protein structure (standalone)
  

  

• PocketLG: Protein Binding Pocket Identification Using Protein Language Model and Graph Transformer
  

  

• PocketQuery: energy-based (online)

  

  

• PocketVec: Comprehensive detection and characterization of human druggable pockets through binding site descriptors

  

• PointSite: a point cloud segmentation tool for identification of protein ligand binding atoms (standalone)
  

  

• PrankWeb 3: accelerated ligand-binding site predictions for experimental and modelled protein structures (online)

  

• ProteinPocketDetection: Protein pocket detection via convex hull surface evolution and associated Reeb graph (standalone)
  

• PyVOL: python library packaged into a PyMOL GUI for identifying protein binding pockets, find volume, also via python command line (standalone)

  

• SGPocket: A Graph Convolutional Neural Network to predict protein binding site.
  

  

• SiteFerret: beyond simple pocket identification in proteins (standalone)
  

  

• SiteHound-web: energy-based (send chemical probes) (online)

  

  

• SplitPocket: geometry-based (online)

  

  

• fpocket: mainly geometry-based (standalone)
  

  

• pyKVFinder: Python package for biomolecular cavity detection and characterization in data science (standalone)
  

  

Allosteric Site Prediction

• APOP: Predicting Allosteric Pockets in Protein Biological Assemblages (standalone)
  

  

• Allo: a tool for dicriminating and prioritizing allosteric pockets (standalone)
  

  

• AlloReverse: AlloReverse predicts multi-scale allosteric regulation information based on reversed allosteric communication theory, aiding in drug design and biological mechanism understanding.

  

  

• AlloSite: allosteric site (online)

  

  

• DeepAllo: DeepAllo: Allosteric Site Prediction using Protein Language Model (pLM) with Multitask Learning
  

  

• MEF-AlloSite: MEF-AlloSite: an accurate and robust Multimodel Ensemble Feature selection for the Allosteric Site identification model
  

  

• PASSer: Designed for accurate allosteric site prediction, PASSer offers three machine learning models for quick and extensive allosteric analysis.

  

  

• PASSerRank: Prediction of allosteric sites with learning to rank (standalone)
  

  

Covalent

• CovCysPredictor: Predicting Selective Covalently Modifiable Cysteines
  

  

Cryptic Pockets

• PocketMiner: predicting the locations of cryptic pockets from single protein structures (standalone)
  

  

Benchmark

• CryptoBench: Cryptic protein-ligand binding sites dataset and benchmark

  

Fragment Site Prediction

• FTMap: FTMap maps unbound protein surfaces to identify druggable hot spots where small molecules may bind.

  

  

From Molecular Dynamics simulations

• CHARMM-GUI LBS Finder & Refiner: Ligand Binding Site Prediction and Refinement (standalone)

• ColaBind: Cloud-Based Approach for Prediction of Binding Sites Using Coarse-Grained Simulations with Molecular Probes
  

  ;)

• POVME2: POVME2 identifies druggable protein pockets and their unique conformations within molecular dynamics simulations, facilitating the discovery of novel pharmacologically active molecules.
  

  

• TACTICS: machine-learning algorithm (trajectory-based analysis of conformations to identify cryptic sites), which uses an ensemble of molecular structures (such as molecular dynamics simulation data) as input (standalone)
  

  

Growth Site Prediction

• SINCHO: SINCHO protocol is a method for the prediction/suggestion of the desirable anchor atom and growth site pair for the modification of the hit compound in hit-to-lead process.
  

  

Metal-binding site

• PinMyMetal: Specific details about PinMyMetal, presumably a tool for predicting metal-binding sites in proteins, were not provided in the data fetched.
  

  

  

Multi-ligand binding residues

• MERIT: MERIT (Multi-binding rEsidues pRedIcTor) accurately predicts MLRBs in protein sequences

  

  

Reviews

• Barton et al.: Comparative evaluation of methods for the prediction of protein-ligand binding sites