Background/Objectives: Accurate drug-target affinity (DTA) prediction supports virtual screening, lead optimization, and drug repurposing. This study investigates whether replacing the conventional post-aggregation multilayer perceptron within a Graph Isomorphism Network drug encoder with a Kolmogorov-Arnold Network (KAN) transformation improves DTA prediction. Methods: The resulting KAN-GIN framework combines molecular-graph representations with a convolutional protein-sequence encoder. It was evaluated on the DAVIS, KIBA, and METZ benchmarks against a matched GIN baseline and representative multiscale and graph-pretraining-based DTA architectures under a common split and evaluation protocol. Complementary analyses examined latent-space organization, gradient-based molecular attribution, and an enhancer of zeste homolog 2 (EZH2) candidate-ranking case study. Results: KAN-GIN consistently reduced prediction error relative to the matched GIN baseline and achieved competitive performance against the additional architectures, including the strongest overall results on METZ. The latent-space findings were dataset-dependent, while gradient-based attribution identified molecular substructures contributing to individual predictions. The EZH2 case study illustrated how KAN-GIN can support candidate ranking before structure-based evaluation. Conclusions: KAN-based post-aggregation transformations represent an emerging design option for graph-based DTA prediction. However, the latent-space and structure-based findings remain computational and should not be interpreted as evidence of mechanistic biological relevance without prospective experimental validation.
Abla Bedoui, Nithyadevi Duraisamy, Mohammed Cherkaoui· Pharmaceuticals· 0 citations
Automated segmentation of the left and right ventricles (LVs and RVs) in cine cardiac MRI (CMR) underpins reliable volumetry and mass estimation. However, papillary muscles and trabeculae (PM/T) introduce clinically meaningful variability and exacerbate cross-dataset domain shift. We present VentrEX, an anatomically guided pipeline. The core segmenter, VentrEX-Seg, is a 3D encoder-decoder with parallel channel-spatial attention and a Transformer bottleneck. Training is performed exclusively on ACDC. A lightweight PM/T module automatically extracts papillary and trabecular burdens and standardizes cavity volumes. External evaluation is zero-shot (no fine-tuning) on Sunnybrook (LV) and MM-WHS MRI (RV). We report Dice, HD95 (mm); for volumetry, we use Bland-Altman analyses (LV and RV volumes). Attention/Grad-CAM visualizations support interpretability. On ACDC, VentrEX achieved higher Dice and lower boundary error than U-Net, nnU-Net, CBAM, and VentrEX-Seg. Zero-shot performance was preserved externally (e.g., Sunnybrook LV Dice 0.9053, HD95 4.95 mm; MM-WHS RV Dice 0.9236, HD95 6.61 mm). Patient-level Bland–Altman analyses characterized LV and RV volumetric agreement. Qualitative overlays and 3D reconstructions showed fewer PM/T “leaks” and anatomically plausible borders across ED/ES. Single-source training with dual zero-shot external tests demonstrates robustness under domain shift. The combination of parallel attention and a Transformer bottleneck enables accurate, transparent cine-CMR segmentation across datasets.
Abla Bedoui, Julieta Anahí Rancati, Ignacio Lugones et al.· Journal of Imaging· 0 citations
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