Multiparameter Persistent Homology for Molecular Property Prediction
This work addresses the problem of molecular property prediction for chemists and drug discovery researchers by offering a more comprehensive and interpretable topological characterization, though it is incremental in advancing persistent homology methods.
The authors tackled molecular property prediction by introducing a novel fingerprint generation method based on multiparameter persistent homology, which captures topological features across multiple scales and parameters like atomic mass and bond type, and demonstrated its effectiveness with experiments on datasets such as Lipophilicity, FreeSolv, and ESOL.
In this study, we present a novel molecular fingerprint generation method based on multiparameter persistent homology. This approach reveals the latent structures and relationships within molecular geometry, and detects topological features that exhibit persistence across multiple scales along multiple parameters, such as atomic mass, partial charge, and bond type, and can be further enhanced by incorporating additional parameters like ionization energy, electron affinity, chirality and orbital hybridization. The proposed fingerprinting method provides fresh perspectives on molecular structure that are not easily discernible from single-parameter or single-scale analysis. Besides, in comparison with traditional graph neural networks, multiparameter persistent homology has the advantage of providing a more comprehensive and interpretable characterization of the topology of the molecular data. We have established theoretical stability guarantees for multiparameter persistent homology, and have conducted extensive experiments on the Lipophilicity, FreeSolv, and ESOL datasets to demonstrate its effectiveness in predicting molecular properties.