RAVEN: Frozen Random Graph Reservoirs with Physics-Informed Interaction Fingerprints for Protein-Ligand Binding Affinity Prediction
2026-08-10 • Machine Learning
Machine Learning
AI summaryⓘ
The authors developed a method called RAVEN to predict how strongly a protein and a molecule will bind together using 3D structures. They use multiple fixed graph encoders to create different views of the molecule’s structure without changing the encoders during training. These views are combined with chemical fingerprints and processed by various prediction models, which are then fused for better accuracy. Their approach worked well on standard protein-ligand datasets, showing it can reliably estimate binding strength. This method combines structural diversity, chemical features, and multiple model types to improve predictions.
protein-ligand binding affinity3D complex structuresgraph encodersphysicochemical interaction fingerprintreservoir computingsupervised learningregressorsPDBbind datasetCASF benchmarkmodel fusion
Authors
Qingyang Zou, Jiaye Huang, Hangbo Xie, Jiayue Yin, Youyi Song, Jinfeng Liu
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.