Source code for CD-BAN, a dual-branch graph neural network with a bilinear cross-attention layer that classifies drug-cyclodextrin inclusion-complex binding affinity (Strong: K > 10,000 M⁻¹ vs. Weak: K < 100 M⁻¹) from 2D SMILES alone, without 3D geometry or handcrafted descriptors. Trained only on the binary extremes, it orders 1,850 withheld intermediate-affinity compounds along a continuous gradient (Kendall τ = -0.289, p < 1e-75); a multi-task regression head converts this into a formula-free affinity estimate. This release contains the complete code, the hyperparameter configuration (configs/CDBAN.yaml), the pinned environment (requirements.txt), and all analysis scripts (no-attention ablation, classical baselines, PCA/UMAP embedding, atom-masking causal validation, host-derivative cold-start, calibration, external-guest similarity). The training data are derived from the OpenCycloDB (Tahil et al. 2023, doi:10.5281/zenodo.7575539) and are referenced, not redistributed, in this code release. Code: https://github.com/CHIHX12/CD-BAN
Source code for CD-BAN, a dual-branch graph neural network with a bilinear cross-attention layer that classifies drug-cyclodextrin inclusion-complex binding affinity (Strong: K > 10,000 M⁻¹ vs. Weak: K < 100 M⁻¹) from 2D SMILES alone, without 3D geometry or handcrafted descriptors. Trained only on the binary extremes, it orders 1,850 withheld intermediate-affinity compounds along a continuous gradient (Kendall τ = -0.289, p < 1e-75); a multi-task regression head converts this into a formula-free affinity estimate. This release contains the complete code, the hyperparameter configuration (configs/CDBAN.yaml), the pinned environment (requirements.txt), and all analysis scripts (no-attention ablation, classical baselines, PCA/UMAP embedding, atom-masking causal validation, host-derivative cold-start, calibration, external-guest similarity). The training data are derived from the OpenCycloDB (Tahil et al. 2023, doi:10.5281/zenodo.7575539) and are referenced, not redistributed, in this code release. Code: https://github.com/CHIHX12/CD-BAN
