# A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design

arXiv: https://arxiv.org/abs/2311.16126 · HTML: https://mlsanity.com/paper/2311.16126/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 70 references that are in the map.
- First posted on arXiv 30 Oct 2023.

## Builds on (most cited first, 23 of 23)

- [E(n) Equivariant Graph Neural Networks](https://mlsanity.com/paper/2102.09844/) (2021)
- [Learning from Protein Structure with Geometric Vector Perceptrons](https://mlsanity.com/paper/2009.01411/) (2020)
- [Evaluating Protein Transfer Learning with TAPE](https://mlsanity.com/paper/1906.08230/) (2019)
- [ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing](https://mlsanity.com/paper/2007.06225/) (2020)
- [Protein Representation Learning by Geometric Structure Pretraining](https://mlsanity.com/paper/2203.06125/) (2022)
- [ProGen: Language Modeling for Protein Generation](https://mlsanity.com/paper/2004.03497/) (2020)
- [Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design](https://mlsanity.com/paper/2110.04624/) (2021)
- [BERTology Meets Biology: Interpreting Attention in Protein Language Models](https://mlsanity.com/paper/2006.15222/) (2020)
- [Conditional Antibody Design as 3D Equivariant Graph Translation](https://mlsanity.com/paper/2208.06073/) (2022)
- [Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs](https://mlsanity.com/paper/2110.01191/) (2021)
- [End-to-End Learning on 3D Protein Structure for Interface Prediction](https://mlsanity.com/paper/1807.01297/) (2018)
- [Structure-aware Protein Self-supervised Learning](https://mlsanity.com/paper/2204.04213/) (2022)
- [Protein Sequence and Structure Co-Design with Equivariant Translation](https://mlsanity.com/paper/2210.08761/) (2022)
- [Contrastive Representation Learning for 3D Protein Structures](https://mlsanity.com/paper/2205.15675/) (2022)
- [DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations](https://mlsanity.com/paper/2204.08672/) (2022)
- [Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model](https://mlsanity.com/paper/2110.15527/) (2021)
- [Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural Information](https://mlsanity.com/paper/1912.05625/) (2019)
- [Pre-training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding](https://mlsanity.com/paper/2204.08663/) (2022)
- [Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement](https://mlsanity.com/paper/2207.06616/) (2022)
- [Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks](https://mlsanity.com/paper/2212.03447/) (2022)
- [Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models](https://mlsanity.com/paper/2012.00195/) (2020)
- [Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport](https://mlsanity.com/paper/2202.06208/) (2022)
- [Instructor-inspired Machine Learning for Robust Molecular Property Prediction](https://mlsanity.com/paper/2304.03906/) (2023)

## Built on by (most cited first, 13 of 13)

- [Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization](https://mlsanity.com/paper/2403.16576/) (2024)
- [PPFlow: Target-aware Peptide Design with Torsional Flow Matching](https://mlsanity.com/paper/2405.06642/) (2024)
- [UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design](https://mlsanity.com/paper/2503.19300/) (2025)
- [An All-Atom Generative Model for Designing Protein Complexes](https://mlsanity.com/paper/2504.13075/) (2025)
- [Retrieval Augmented Diffusion Model for Structure-informed Antibody Design and Optimization](https://mlsanity.com/paper/2410.15040/) (2024)
- [Relation-Aware Equivariant Graph Networks for Epitope-Unknown Antibody Design and Specificity Optimization](https://mlsanity.com/paper/2501.00013/) (2024)
- [Proteo-R1: Reasoning Foundation Models for De Novo Protein Design](https://mlsanity.com/paper/2605.02937/) (2026)
- [AbBiBench: A Benchmark for Antibody Binding Affinity Maturation and Design](https://mlsanity.com/paper/2506.04235/) (2025)
- [Unified all-atom molecule generation with neural fields](https://mlsanity.com/paper/2511.15906/) (2025)
- [Surface-based Molecular Design with Multi-modal Flow Matching](https://mlsanity.com/paper/2601.04506/) (2026)
- [Dynamics-inspired Structure Hallucination for Protein-protein Interaction Modeling](https://mlsanity.com/paper/2601.06214/) (2026)
- [Decoupled Sequence and Structure Generation for Realistic Antibody Design](https://mlsanity.com/paper/2402.05982/) (2024)
- [SurfDesign: Effective Protein Design on Molecular Surfaces](https://mlsanity.com/paper/2606.07567/) (2026)

## Abstract (arXiv)

Therapeutic antibodies are an essential and rapidly expanding drug modality. The binding specificity between antibodies and antigens is decided by complementarity-determining regions (CDRs) at the tips of these Y-shaped proteins. In this paper, we propose a hierarchical training paradigm (HTP) for the antibody sequence-structure co-design. HTP consists of four levels of training stages, each corresponding to a specific protein modality within a particular protein domain. Through carefully crafted tasks in different stages, HTP seamlessly and effectively integrates geometric graph neural networks (GNNs) with large-scale protein language models to excavate evolutionary information from not only geometric structures but also vast antibody and non-antibody sequence databases, which determines ligand binding pose and strength. Empirical experiments show that HTP sets the new state-of-the-art performance in the co-design problem as well as the fix-backbone design. Our research offers a hopeful path to unleash the potential of deep generative architectures and seeks to illuminate the way forward for the antibody sequence and structure co-design challenge.