MolPIF: a parameter interpolation flow model for molecule generation
Yaowei Jin1, Junjie Wang1,2, Yufan Tang3
1Lingang Laboratory, Shanghai 200031, China.
Bioinformatics (Oxford, England)
|May 25, 2026
Summary
MolPIF unifies continuous and discrete molecular generation for structure-based drug design. This novel flow mechanism improves binding affinity and chemical validity, advancing generative models for drug discovery.
Area of Science:
- Computational chemistry
- Molecular modeling
- Drug discovery
Background:
- Deep generative models show promise for structure-based drug design (SBDD).
- A key challenge is unifying continuous atomic coordinates with discrete atom types.
- Existing methods struggle with heterogeneous molecular data, limiting generative model performance.
Purpose of the Study:
- To introduce MolPIF, a novel parameter interpolation flow mechanism.
- To unify the generation of continuous and discrete molecular variables in SBDD.
- To enhance the capture of atomic contexts through geometry-enhanced learning.
Main Methods:
- MolPIF interpolates between distributions in parameter space, not sample space.
- Theoretically recovers Wasserstein-2 optimal transport for continuous coordinates.
- Establishes Fisher-Rao geodesics for discrete atom types, incorporating geometry-enhanced learning.
Main Results:
- MolPIF outperforms existing methods on the CrossDocked2020 dataset.
- Demonstrates superior binding affinity, chemical validity, and geometric fidelity.
- Shows strong performance in chemical space coverage and lead optimization.
Conclusions:
- MolPIF establishes a robust paradigm for SBDD by unifying molecular variable generation.
- The method offers flexibility in prior distribution selection.
- MolPIF advances generative modeling for protein-ligand complex design.
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