Docking

• OpenVS: An artificial intelligence accelerated virtual screening platform for drug discovery
  

  

All-Atom-DL methods

• AlphaFold3: Accurate structure prediction of biomolecular interactions with AlphaFold 3

  

  

• Boltz-1: Boltz-1 is an open-source model which predicts the 3D structure of proteins, RNA, DNA and small molecules
  

  

• DiffusionProteinLigand: End-to-end protein–ligand complex structure generation with diffusion-based generative models (standalone).
  

  

• DockFormer: employs fully flexible docking, where no part of the receptor remains rigid, by adapting the AlphaFold2 architecture
  

  

• DynamicBind: DynamicBind recovers ligand-specific conformations from unbound protein structures (e.g. AF2-predicted structures), promoting efficient transitions between different equilibrium states.
  

  

• FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction
  

  

• NeuralPlexer: deep generative model to jointly predict protein-ligand complex 3D structures and beyond.
  

  

• RoseTTAFold-AllAtom: RoseTTAFold All-Atom (RFAA) is a deep network capable of modeling full biological assemblies containing proteins, nucleic acids, small molecules, metals, and covalent modifications with high accuracy.

  

Allosteric sites

• FASTDock: FASTDock is a pipeline for allosteric drug discovery, offering scripts and a Jupyter notebook for efficiently generating and analyzing docking grids, clusters, and fingerprint screenings.
  

  

Blind Docking

• CBDock2: CBDock2 is an improved protein-ligand blind docking tool integrating cavity detection, docking, and homologous template fitting to suggest novel therapeutic targets for biological and pharmaceutical studies.

  

  

• CoBDock: CoBDock is a reference implementation of the COBDock algorithm, detailing steps for setup and execution on Linux, with a focus on integrating various molecular docking and pocket identification algorithms.
  

Chemical Space Docking

• SpaceHASTEN: SpaceHASTEN: A structure-based virtual screening tool for non-enumerated virtual chemical libraries
  

  

Classical

• AutoDock Vina: AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings
  

  

• BR-NiB: Brute Force Negative Image-Based Optimization. A docking rescoring method that relyes on shape/ electrostatic potential similarity between the docking poses of ligands and the cavity-based negative images (standalone).
  

  

• DSDP: DSDP: A Blind Docking Strategy Accelerated by GPUs
  

  

• DockThor:

  

• FWAVina: The content for FWAVina was not accessible due to restrictions or an error from the URL provided.
  

• GLOW-IVES: Provides Python implementation of GLOW (auGmented sampLing with softened vdW potential) and IVES (Iterative Ensemble Sampling) protocols for pose sampling, along with new cross-docking datasets.
  

• GeauxDock: An ultra-fast automated docking program from LSU, predicting how small ligands bind to macromolecules using a novel hybrid force field and a Monte Carlo protocol.
  

  

• HESS: A new protein-ligand docking software with an improved method of molecular conformation optimization
  

  

• JAMDA: Redocking the PDB

  

  

• MzDock: MzDOCK: A free ready‐to‐use GUI‐based pipeline for molecular docking simulations
  

  

• Opendock: OpenDock: A pytorch-based open-source framework for protein-ligand docking and modelling
  

  

• ProBiS-Dock: A Hybrid Multitemplate Homology Flexible Docking Algorithm Enabled by Protein Binding Site Comparison (standalone).
  

  

• QVINA: QuickVina 2 aims to accurately speed up AutoDock Vina, providing up to 20.49-fold acceleration with high correlation in binding energy prediction.
  

  

• RDPSO_Vina: A fast docking tool utilizing random drift particle swarm optimization based on the AutoDock Vina and PSOVina framework.
  

  

• RxDock: a fast, versatile, and open-source program for docking ligands to proteins and nucleic acids (standalone).
  

  

• SMINA: A fork of AutoDock Vina that supports scoring function development and high-performance energy minimization, maintained by the University of Pittsburgh.
  

  

• Surflex-Tools: starting with version 4 (standalone).

• Uni-Dock: Uni-Dock is a GPU-accelerated molecular docking program that supports various scoring functions and achieves significant speed-up compared with AutoDock Vina on a single CPU core.
  

  

• VinaCarb: The content for VinaCarb was not available from the URL provided.

  

• VinaGPU2.0: Vina-GPU 2.0: Further Accelerating AutoDock Vina and Its Derivatives with Graphics Processing Units
  

  

• VinaXB (halogen-bonding): Introduces a halogen bonding scoring function (XBSF) in AutoDock Vina, termed AutoDock VinaXB, to improve docking accuracy with halogenated ligands.
  

  

• Xdock: versatile docking framework built for both protein–ligand and nucleic acid–ligand interactions
  

  

  

• labodock: LABODOCK offers a collection of Jupyter Notebooks for molecular docking on Google Colab with minimal coding, streamlining pre- and post-docking processes.
  

  

• parallel-PLANTS: Offers a method for parallel molecular docking using the PLANTS software, aimed at academic use.
  

• restretto: Effective Protein–Ligand Docking Strategy via Fragment Reuse and a Proof-of-Concept Implementation (standalone).
  

  

• vina4dv:
  

Consensus

• DockingPie: DockingPie is a PyMOL plugin that facilitates consensus docking and scoring analyses, integrating four docking programs (Smina, Autodock Vina, RxDock, and ADFR) to offer a versatile platform for molecular and consensus docking.
  

  

• Exponential Consensus Ranking:

  

• VoteDock: VoteDock: Consensus docking method for prediction of protein–ligand interactions

  

• dockECR: dockECR: Open consensus docking and ranking protocol for virtual screening of small molecules
  

  

Reviews

• Multi-Criteria Decision Analysis in Drug Discovery:

  

Flexible Docking

• ADFR: AutoDockFR is a protein-ligand docking program supporting selective receptor flexibility and covalent docking, part of the ADFR suite for streamlined docking procedures.

  

• ApoDock:
  

  

• DSDPFlex: DSDPFlex: An Improved Flexible-Receptor Docking Method with GPU Acceleration
  

  

• FlexAID: FlexAID: Revisiting Docking on Non-Native-Complex Structures
  

  

• GNINA: GNINA is a molecular docking program that incorporates scoring and optimization of ligands using convolutional neural networks, aiming to combine the versatility of smina and AutoDock Vina with the predictive power of deep learning.
  

  

• PackDock: Describes PackDock as a diffusion-based side chain packing model for flexible protein-ligand docking, indicating code will be available following the publication of their paper.
  

  

• hybrid-SA-IFD: Robust Induced Fit Docking Approach with the Combination ofthe Hybrid All-Atom/United-Atom/Coarse-Grained Model andSimulated Annealing
  

• iDock: iDock is a multithreaded virtual screening tool for flexible ligand docking in computational drug discovery, inspired by AutoDock Vina and hosted on GitHub under Apache License 2.0.
  

  

• tiny_IFD: Offers lightweight induced fit docking capabilities.
  

Fragment-based

• Spresso: an ultrafast compound pre-screening method based on compound decomposition

  

HPC enabled

• EasyDock: EasyDock is a fully automatic pipeline for molecular docking, featuring a simple setup for using vina and gnina/smina for docking operations, intended to streamline the docking process.
  

  

• POAP: POAP: A GNU parallel based multithreaded pipeline of open babel and AutoDock suite for boosted high throughput virtual screening
  

  

• VinaLC:
  

  

• VinaMPI: VinaMPI: Facilitating multiple receptor high-throughput virtual docking on high-performance computers

  

• VinaSC: VinaSC: Scalable Autodock Vina with fine-grained scheduling on heterogeneous platform

  

MD-based

• ColDock: ColDock: Concentrated Ligand Docking with All-Atom Molecular Dynamics Simulation

  

ML-based

• AQDNet: Implements a Deep Neural Network for Protein-Ligand Docking Simulation, focusing on identifying correct binding poses through convolutional neural network approaches.
  

  

• ArtiDock: ArtiDock: fast and accurate machine learning approach to protein-ligand docking based on multimodal data augmentation

  

• CarsiDock: a deep learning paradigm for accurate protein–ligand docking and screening based on large-scale pre-training (standalone, 2024).
  

  

• DeepBindBC: A practical deep learning method for identifying native-like protein-ligand complexes in virtual screening (online).

  

• DeltaDock: DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking
  

  

• DiffBindFR: DiffBindFR: an SE(3) equivariant network for flexible protein–ligand docking
  

  

• DiffDock: A state-of-the-art method for molecular docking, incorporating diffusion steps and a significant improvement in performance and generalization capacity.
  

  

• DiffDock-NMDN: Normalized Protein–Ligand Distance Likelihood Score for End-to-End Blind Docking and Virtual Screening
  

  

• DiffDock-Pocket:
  

  

• EDM-Dock: Efficient and accurate large library ligand docking with KarmaDock
  

  

• ESF - scalar fields: Implements Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms, a machine learning-based ligand pose scoring function for rapid optimization.
  

  

• EViS: EViS is an enhanced virtual screening method integrating ligand docking, protein pocket template searching, and ligand template shape similarity calculations, utilizing a novel PL-score for evaluation.
  

  

• FABind: FABind: Fast and Accurate Protein-Ligand Binding
  

  

• FABind+: FABind: Fast and Accurate Protein-Ligand Binding
  

  

• FeatureDock: Protein-Ligand Docking Guided by Physicochemical Feature-Based Local Environment Learning using Transformer
  

  

• FlexPose: Equivariant Flexible Modeling of the Protein–Ligand Binding Pose with Geometric Deep Learning
  

  

• GAABind: GAABind is a Geometry-Aware Attention-Based Network for accurate protein-ligand binding pose and binding affinity prediction, featuring a comprehensive environment setup and dataset processing guide.
  

  

• GNINA: GNINA is a molecular docking program that incorporates scoring and optimization of ligands using convolutional neural networks, aiming to combine the versatility of smina and AutoDock Vina with the predictive power of deep learning.
  

  

• GNINA_KD: Condensing Molecular Docking CNNs via Knowledge Distillation
  

  

• GalaxyDock-DL: Protein–Ligand Docking by Global Optimization and Neural Network Energy
  

  

• Interformer: interaction-aware model for protein-ligand docking and affinity prediction
  

  

• KarmaDock: Efficient and accurate large library ligand docking with KarmaDock
  

  

• LigPose: One-step Structure Prediction and Screening for Protein-Ligand Complexes using Multi-Task Geometric Deep Learning
  

  

• PGBind: pocket-guided explicit attention learning for protein–ligand docking
  

  

• PLANTAIN: Predicting LigANd pose wiTh an AI scoring functioN
  

  

• PPDock: Pocket Prediction-Based Protein–Ligand Blind Docking
  

  

• PointVS: SE(3)-equivariant point cloud networks designed for virtual screening, enabling E(3)-invariant predictions of binding pose and affinity using networks based on the EGNN graph neural network layer.
  

  

• QuickBind: QuickBind: A Light-Weight And Interpretable Molecular Docking Model
  

  

• RapidDock: RapidDock: Unlocking Proteome-scale Molecular Docking

  

• Re-Dock: Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge

  

• SurfDock: Surface-Informed Diffusion Generative Model for Reliable and Accurate Protein-ligand Complex Prediction
  

  

• TankBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction
  

  

• TopoFormer: A topological transformer for protein-ligand complex interaction prediction, integrating multiscale topology techniques with a structure-to-sequence transformer model.
  

  

• Uni-Dock: GPU-Accelerated Docking Enables Ultralarge Virtual Screening (standalone).
  

  

• Uni-Mol: A Universal 3D Molecular Representation Learning Framework
  

  

• Uni-Mol v2:
  

  

• gnina-torch: A PyTorch implementation of the GNINA molecular docking scoring function, designed for enhanced performance and adaptability.
  

  

• vScreenML: A machine learning classifier designed for virtual screening, allowing for the rescoring of hits to eliminate false positives, based on the Dataset of Congruent Inhibitors and Decoys (D-COID).
  

  

• vScreenML2.0: Improved Machine Learning Classification for Reducing False Positives in Structure-Based Virtual Screening
  

  

Flexible

• CarsiDock-Flex:
  

  

MetalloProteins

• MetalDock: MetalDock: An Open Access Docking Tool for Easy and Reproducible Docking of Metal Complexes
  

  

Multi-Ligand

• HARMONICFlow: Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design
  

  

Negative Image based

• O-LAP: Building shape-focused pharmacophore models for effective docking screening
  

  

• PANTHER: Negative Image based docking and scoring

  

Pose Optimisation

• DeepRMSD-Vina: DeepRMSD+Vina is a computational framework integrating ligand binding pose optimization and screening, utilizing deep learning alongside the classical Vina scoring function.
  

  

Protein Docking

• EquiDock: EquiDock employs geometric deep learning for fast and accurate rigid 3D protein-protein docking, focusing on efficiency and accessibility with comprehensive preprocessing and training guidelines.
  

  

• LightDock: The open-source macromolecular docking framework written in Python
  

  

• PyDock3: Electrostatics and desolvation for effective scoring of rigid-body protein-protein docking
  

  

Quantum

• Zhang et al.: Encoding Molecular Docking for Quantum Computers

  

Reviews

• Revolutionizing drug discovery: an AI-powered transformation of molecular docking: Revolutionizing drug discovery: an AI-powered transformation of molecular docking

  

Shape-based

• PheSA: An Open-Source Tool for Pharmacophore-Enhanced Shape Alignment
  

  

Template

• FitDock: fits initial conformation to the given template using a hierachical multi-feature alignment approach, subsequently explores the possible conformations, and finally outputs refined docking poses

  

Water

• WatVina: Watvina facilitates drug design with support for explicit or implicit waters, pharmacophore, or position-constrained docking, and external torsion parameters, enhancing the Autodock Vina engine.
  

WebServers

• MolModa: accessible and secure molecular docking in a web browser.

  

• TOMEv3: @TOME 3.0: Interfacing Protein Structure Modeling and Ligand Docking