Changelog

Version 4.0.0 (2026)

Major Features:

• State-of-the-Art Performance: Improved GNN training achieves:

  • Pearson R = 0.88 on PDBbind (5,316 complexes)

  • Pearson R = 0.82 on ULVSH held-out test set

  • Pearson R = 0.68 on novel GABA receptor dataset (30 compounds)

• Universal GNN Rescorer: New pandadock gnn rescore command

  • Rescore poses from ANY docking software (Vina, Glide, GOLD, etc.)

  • Input: Standard SDF file with multiple conformers

  • Output: CSV with GNN scores + annotated SDF

• Model Download: New pandadock gnn download-model command

  • Download pre-trained model from GitHub releases

  • No training required for quick start

• Combined Training: Train on merged PDBbind + ULVSH datasets

  • Unified pK scale across different affinity types

  • Better generalization to novel targets

CLI Additions:

• pandadock gnn rescore - Universal pose rescoring

• pandadock gnn download-model - Pre-trained model download

Documentation:

• Complete publication manuscript with algorithms and benchmarks

• Updated benchmark tables with improved performance metrics

Version 3.0.0 (2024)

Major Features:

• PandaDock-GNN: New SE(3)-equivariant Graph Neural Network scoring function

  • Achieves Pearson R = 0.67 on ULVSH benchmark

  • Outperforms all 8 baseline scoring methods

  • Multi-task learning: pEC50 regression + activity classification

  • Heterogeneous graph representation (protein + ligand nodes)

• Hybrid Docking Workflow: New pandadock hybrid command

  • Combines traditional pose generation with GNN rescoring

  • Recommended approach for best accuracy

  • Generates diverse poses, rescores with GNN, ranks by pEC50

• Vina-Style Scoring: New default scoring function

  • Implements AutoDock Vina empirical weights

  • Gaussian steric interactions, hydrophobic, hydrogen bonding

  • Rotatable bond flexibility penalty

CLI Changes:

• New commands:

  • pandadock hybrid - Hybrid docking with GNN rescoring

  • pandadock gnn train - Train GNN model

  • pandadock gnn predict - Predict binding affinity

  • pandadock gnn benchmark - Benchmark model performance

  • pandadock gnn compare - Compare against baselines

• Simplified algorithm selection:

  • Single pandadock algorithm (hierarchical search)

  • Removed broken GPU algorithms

  • Default scoring changed to vina

Architecture Changes:

• Simplified algorithm module: only HierarchicalDocker retained

• Added VinaScoring class with standard Vina weights

• GNN module structure: data/, models/, training/, scoring.py

• Multi-format molecule parsing: MOL2, PDB, SDF

Bug Fixes:

• Fixed edge dimension mismatch in GNN (23 dims, not 21)

• Fixed target tensor squeeze issue in training

• Fixed checkpoint saving (ModelConfig vs TrainingConfig)

• Fixed scatter dtype mismatch on CUDA with AMP

Dependencies:

• Added optional [gnn] extras: torch, torch-geometric, torch-scatter, torch-sparse

• Updated version to 3.0.0

Version 2.x

Previous versions focused on traditional docking algorithms.

Version 1.x

Initial release with basic docking functionality.