# Simple End-to-end Deep Learning Model for CDR-H3 Loop Structure Prediction

arXiv: https://arxiv.org/abs/2111.10656 · HTML: https://mlsanity.com/paper/2111.10656/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 20 Nov 2021.

## Builds on (most cited first, 1 of 1)

- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)

## Built on by (most cited first, 1 of 1)

- [A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generation](https://mlsanity.com/paper/2402.08703/) (2024)

## Abstract (arXiv)

Predicting a structure of an antibody from its sequence is important since it allows for a better design process of synthetic antibodies that play a vital role in the health industry. Most of the structure of an antibody is conservative. The most variable and hard-to-predict part is the third complementarity-determining region of the antibody heavy chain (CDR H3). Lately, deep learning has been employed to solve the task of CDR H3 prediction. However, current state-of-the-art methods are not end-to-end, but rather they output inter-residue distances and orientations to the RosettaAntibody package that uses this additional information alongside statistical and physics-based methods to predict the 3D structure. This does not allow a fast screening process and, therefore, inhibits the development of targeted synthetic antibodies. In this work, we present an end-to-end model to predict CDR H3 loop structure, that performs on par with state-of-the-art methods in terms of accuracy but an order of magnitude faster. We also raise an issue with a commonly used RosettaAntibody benchmark that leads to data leaks, i.e., the presence of identical sequences in the train and test datasets.