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This slide demonstrates the quality of our embedding space through t-SNE visualization and quantitative metrics. The visualization results show a clear distinction between our geometric and text embeddings. When we look at the text embeddings on the left, we see mixed clustering with significant overlap between the three CYP enzyme types. However, the geometric embeddings on the right show distinct separation with clear boundaries between CYP3A4, CYP2D6, and CYP2C19. Quantitatively, this is reflected in our silhouette scores - geometric embeddings achieve 0.73 compared to 0.58 for text embeddings, representing 85% better cluster separation with structural information. Our cross-validation enzyme classification accuracy reaches 89.3%. The scientific significance is profound - structural embeddings successfully capture enzyme-specific binding patterns, enable accurate enzyme family classification, and provide interpretable similarity measurements for drug design. This represents the first demonstration that geometric protein embeddings outperform text-only approaches for enzyme classification. |
IEEE/ICACT20260263 Slide.08
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