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This slide provides a deeper analysis of our embedding space quality. The t-SNE visualization results clearly show the difference between text and geometric embeddings. With text embeddings, we observe mixed clustering with significant overlap between enzyme families. In contrast, geometric embeddings produce three distinct clusters - CYP3A4 in green, CYP2D6 in red, and CYP2C19 in blue - with minimal overlap and clear boundaries. Our quantitative clustering metrics confirm this visual analysis. The silhouette score for geometric embeddings is 0.73 versus 0.58 for text embeddings, again showing 85% better cluster separation. The classification accuracy across all three enzyme types reaches 89.3% in cross-validation. The scientific significance is clear: structural information captures enzyme-specific patterns more effectively than text alone, provides superior performance for enzyme classification tasks, and enables accurate structure-function relationships. This technical innovation represents the first successful demonstration of geometric protein embeddings outperforming traditional text-only approaches for enzyme classification. |
IEEE/ICACT20260263 Slide.09
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