# Attentive cross-modal paratope prediction Source: [Attentive cross-modal paratope prediction]() Collection index: [llms.txt]() Data updated: 2025-12-25T15:36:07.444Z Authors: Andreea Deac, Petar Veli\\v{c}kovi\\'c, Pietro Sormanni Published: 2018-06-12T08:58:19.000Z ## Abstract Antibodies are a critical part of the immune system, having the function of directly neutralising or tagging undesirable objects (the antigens) for future destruction. Being able to predict which amino acids belong to the paratope, the region on the antibody which binds to the antigen, can facilitate antibody design and contribute to the development of personalised medicine. The suitability of deep neural networks has recently been confirmed for this task, with Parapred outperforming all prior physical models. Our contribution is twofold: first, we significantly outperform the computational efficiency of Parapred by leveraging \\\`a trous convolutions and self-attention. Secondly, we implement cross-modal attention by allowing the antibody residues to attend over antigen residues. This leads to new state-of-the-art results on this task, along with insightful interpretations. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Antibody paratope prediction on SAbDab (10-fold cross-validation)](): Antibody paratope prediction; SAbDab (10-fold cross-validation); ROC AUC: 89.9. - [Single Paratope Prediction on SAbDab](): Single Paratope Prediction; SAbDab; AUC: 88.3. ## Original sources and implementation [Original paper]() [PDF]()