RAVEN: Frozen Random Graph Reservoirs with Physics-Informed Interaction Fingerprints for Protein-Ligand Binding Affinity Prediction

2026-08-09

AI Summary

Accurate prediction of protein–ligand binding affinity is hindered by data scarcity, experimental heterogeneity, and conformational dependence. This work proposes a novel paradigm that leverages a multi-head frozen stochastic atom graph encoder to generate diverse structural representations, integrates explicit physicochemical interaction fingerprints, and employs a heterogeneous regressor combining neural networks and tree-based models. To enhance generalization and robustness, the approach avoids end-to-end training and instead adopts a validation-based non-negative fusion strategy. Evaluated on the GEMS-reconstructed PDBbind 2020R1 similarity-isolated split and the CASF-2016 benchmark, the method demonstrates consistently strong and stable predictive performance.

Abstract

Quantitative estimation of protein-ligand binding affinity from three-dimensional complex structures is a fundamental task in structure-based computational chemistry and molecular modeling. Reliable prediction remains challenging because available structure-affinity data are limited, experimentally heterogeneous, conformation-dependent, and sensitive to dataset partitioning. RAVEN (Randomized Atomistic Views with Ensemble Neural Reservoirs) utilizes a multihead reservoir of independently initialized and fully frozen atomistic graph encoders to generate diverse structural projections without end-to-end optimization of the graph representation. These projections are integrated with a deterministic physicochemical interaction fingerprint and processed by heterogeneous supervised readers, including neural and tree-based regressors, whose outputs are combined through validation-based nonnegative fusion. The random reservoir expands structural feature coverage across independent encoder realizations, whereas the explicit physicochemical descriptors and heterogeneous readers contribute complementary information and distinct inductive biases. Evaluation on a similarity-isolated PDBbind 2020R1 split reconstructed using GEMS similarity resources, together with the protected CASF-2016 subset, demonstrated strong predictive performance. The results indicate that frozen multi-view graph representations, explicit physicochemical statistics, and heterogeneous model fusion provide a robust and flexible framework for protein-ligand binding-affinity prediction.

Problem

• protein-ligand binding affinity
• structure-based prediction
• computational chemistry
• molecular modeling
• binding affinity prediction

Innovation

• frozen graph reservoir
• physics-informed interaction fingerprint
• heterogeneous model fusion
• multi-view representation
• binding affinity prediction