CAML-RNA: Commutative Algebra Machine Learning for RNA-Ligand Binding Affinity Prediction

We introduce commutative algebra machine learning for RNA (CAML-RNA), a framework that combines bipartite persistent homology (PH) and persistent Stanley-Reisner theory (PSRT) for predicting RNA-small-molecule binding affinities. RNA-ligand interactions are represented as bipartite atom-pair point clouds, enabling element-specific (ES, 36 pairs, 4 RNA × 9 ligand elements) and category-specific (CS, 36 pairs, 4 RNA structural categories × 9 ligand elements) Vietoris-Rips filtrations that extract β₀ and β₁ Betti curves (PH features, 3888-dim combined), supplemented by persistent f-vectors and h-vectors from a true bipartite distance filtration (PSRT features, 3744-dim, following Suwayyid and Wei). Applied to a curated benchmark of 143 RNA-ligand complexes spanning seven structurally distinct subtypes, CAML-RNA achieves Pearson's R = 0.7283 (RMSE = 1.08 pK_d units) under leave-one-out cross-validation and R = 0.6255 under nested 10-fold cross-validation (Supporting Information), outperforming AffiGrapher (R = 0.498), RLaffinity (R = 0.559), and RLASIF (R = 0.666, all LOO-CV). A subtype-aware feature selection strategy achieves R = 0.940 for aptamers and R = 0.771 for the riboswitch family.