# Adaptive Protein Design Protocols and Middleware

arXiv: https://arxiv.org/abs/2510.06396 · HTML: https://mlsanity.com/paper/2510.06396/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Design and Performance Characterization of RADICAL-Pilot on Leadership-class Platforms](https://mlsanity.com/paper/2103.00091/) (2021)
- [AI-coupled HPC Workflow Applications, Middleware and Performance](https://mlsanity.com/paper/2406.14315/) (2024)
- [RADICAL-Cybertools: Middleware Building Blocks for Scalable Science](https://mlsanity.com/paper/1904.03085/) (2019)
- [Middleware Building Blocks for Workflow Systems](https://mlsanity.com/paper/1903.10057/) (2019)

## Abstract (arXiv)

Computational protein design is experiencing a transformation driven by AI/ML. However, the range of potential protein sequences and structures is astronomically vast, even for moderately sized proteins. Hence, achieving convergence between generated and predicted structures demands substantial computational resources for sampling. The Integrated Machine-learning for Protein Structures at Scale (IMPRESS) offers methods and advanced computing systems for coupling AI to high-performance computing tasks, enabling the ability to evaluate the effectiveness of protein designs as they are developed, as well as the models and simulations used to generate data and train models. This paper introduces IMPRESS and demonstrates the development and implementation of an adaptive protein design protocol and its supporting computing infrastructure. This leads to increased consistency in the quality of protein design and enhanced throughput of protein design due to dynamic resource allocation and asynchronous workload execution.