How paddlepaddle/paddlehelix works
PaddleHelix is a research toolkit of biomolecular machine-learning applications, rather than one end-user service. A representative workflow is protein sequence input, alignment and feature preparation, then structure prediction; the repository also contains separate tools for docking, molecular generation, affinity and synergy prediction, and molecular pretraining. The README documents these capabilities, while sampled source shows parts of the folding data pipeline. Other application internals and their runtime wiring are not sampled, so those links are omitted or marked unverified.
Main entry points: pipeline_parallel.py Alignment pipeline · input_pipeline.py Input features · helixfold HelixFold models · inference_dataset.py HelixDock
Components 18
Protein folding
- Protein sequence
- Alignment pipeline
apps/protein_folding/HelixFold-S1/helixfold/data/pipeline_parallel.py - Input features
apps/protein_folding/HelixFold-S1/helixfold/data/input/input_pipeline.py - HelixFold models
apps/protein_folding/HelixFold-S1/helixfold - Predicted structure
- HelixFold3 pipelines
apps/protein_folding/helixfold3/helixfold/data/msa_pipeline_protein.py - HelixFold-Single
apps/protein_folding/helixfold-single/alphafold_paddle/data/pipeline.py
Drug discovery
- HelixDock
apps/molecular_docking/helixdock/src/inference_dataset.py - Docking prediction
- Drug-target prediction
apps/drug_target_interaction/batchdta - Drug synergy
apps/drug_drug_synergy/DTSyn/main.py - Cancer response
apps/cancer_drug_response/STR/main.py
Molecular learning
- Molecular generation
apps/molecular_generation/JT_VAE/sample.py - Compound pretraining
apps/pretrained_compound/ChemRL/GEM-2/src/model.py - Few-shot properties
apps/fewshot_molecular_property/main.py
Research applications
- CamE model
research/CamE/model.py - Geometry dataset
research/geomgcl/dataset.py
Other components
- Researcher
Connections 17
- Researcher → Protein sequence: provides sequence
- Protein sequence → Alignment pipeline: aligns sequence
- Alignment pipeline → Input features: assembles features
apps/protein_folding/HelixFold-S1/helixfold/data/pipeline_parallel.py - Input features → HelixFold models: feeds model
apps/protein_folding/HelixFold-S1/helixfold/data/input/input_pipeline.py - HelixFold models → Predicted structure: predicts structure
README.md - Researcher → HelixFold3 pipelines: uses prediction
README.md - Researcher → HelixFold-Single: uses prediction
README.md - Researcher → HelixDock: runs docking
README.md - HelixDock → Docking prediction: predicts pose
README.md - Researcher → Drug-target prediction: estimates affinity
README.md - Researcher → Drug synergy: predicts synergy
README.md - Researcher → Cancer response: predicts response
- Researcher → Molecular generation: generates molecules
README.md - Researcher → Compound pretraining: pretrains compounds
README.md - Researcher → Few-shot properties: predicts properties
README.md - Researcher → CamE model: uses research model
- Researcher → Geometry dataset: uses research data