Persistent spectral-based machine learning (PerSpect ML) for protein-ligand binding affinity prediction

Molecular descriptors are essential to not only quantitative structure-activity relationship (QSAR) models but also machine learning-based material, chemical, and biological data analysis. Here, we propose persistent spectral-based machine learning (PerSpect ML) models for drug design. Different from all previous spectral models, a filtration process is introduced to generate a sequence of spectral models at various different scales. PerSpect attributes are defined as the function of spectral variables over the filtration value. Molecular descriptors obtained from PerSpect attributes are combined with machine learning models for protein-ligand binding affinity prediction. Our results, for the three most commonly used databases including PDBbind-2007, PDBbind-2013, and PDBbind-2016, are better than all existing models, as far as we know. The proposed PerSpect theory provides a powerful feature engineering framework. PerSpect ML models demonstrate great potential to significantly improve the performance of learning models in molecular data analysis.

Automated summary

Molecular descriptors are crucial for quantitative structure-activity relationship (QSAR) models and machine learning-based data analysis. The proposed PerSpect ML models utilize a novel filtration process to generate spectral models at various scales, showing potential to greatly improve learning models in molecular data analysis. Results demonstrate superior performance in protein-ligand binding affinity prediction compared to existing models across commonly used databases.