An Interpretable Framework Applying Protein Words to Predict Protein-Small Molecule Complementary Pairing Rules
Provides an interpretable alternative to black-box models for drug discovery, enabling extraction of complementary pairing rules without structural guidance.
The PWRules framework uses binding affinity data to identify privileged small molecule fragments and define complementary pairing rules with protein words, achieving competitive performance to physics-based and deep learning models (e.g., Glide, PSICHIC) on benchmark datasets and showing broad applicability, including SARS-CoV-2 main protease.
Despite the high accuracy of 'black box' deep learning models, drug discovery still relies on protein-ligand interaction principles and heuristics. To improve interpretability of protein-small molecule binding predictions, we developed the PWRules framework, which applies binding affinity data to identify privileged small molecule fragments and subsequently defines complementary pairing rules between these fragments and protein words (semantic sequence units) through an interpretability module. The resulting word-fragment rules are then ranked by the PWScore function to prioritize active compounds. Evaluations on benchmark datasets show that PWScore achieves competitive performance comparable to the physics-based model (Glide) and the deep learning model (PSICHIC) and shows broad applicability for protein targets outside the training dataset, e.g., SARS-CoV-2 main protease. Notably, PWScore captures complementary interaction information, yielding superior enrichment performance when integrated with these established methods. Structural analysis of protein-ligand complexes indicates that learned word-fragment rules are significantly enriched near ligand-binding pockets, despite training without explicit structural guidance. By extracting and applying complementary pairing rules, PWRules provides an interpretable framework for drug discovery.