DeepSeek model analysis and its applications in AI-assistant protein engineering

LI Mingchen; ZHONG Bozitao; YU Yuanxi; JIANG Fan; ZHANG Liang; TAN Yang; YU Huiqun; FAN Guisheng; HONG Liang

doi:10.12211/2096-8280.2025-041

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DeepSeek model analysis and its applications in AI-assistant protein engineering

Invited Review | 更新时间：2025-07-07

- DeepSeek model analysis and its applications in AI-assistant protein engineering
- Synthetic Biology Journal Vol. 6, Issue 3, Pages: 636-650(2025)
- 作者机构：
  
  1.上海交通大学张江高等研究院，上海 201203
  2.华东理工大学信息科学与工程学院，上海 200237
- 作者简介：
- 基金信息：
- DOI：10.12211/2096-8280.2025-041
  CLC： Q816
- Received：01 May 2025，
  
  Revised：2025-06-03，
  
  Published：30 June 2025
- 稿件说明：
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李明辰，钟博子韬，余元玺，姜帆，张良，谭扬，虞慧群，范贵生，洪亮. DeepSeek模型分析及其在AI辅助蛋白质工程中的应用［J］. 合成生物学， 2025， 6（3）： 636-650

LI Mingchen， ZHONG Bozitao， YU Yuanxi， JIANG Fan， ZHANG Liang， TAN Yang， YU Huiqun， FAN Guisheng， HONG Liang. DeepSeek model analysis and its applications in AI-assistant protein engineering［J］. Synthetic Biology Journal， 2025， 6（3）： 636-650
李明辰，钟博子韬，余元玺，姜帆，张良，谭扬，虞慧群，范贵生，洪亮. DeepSeek模型分析及其在AI辅助蛋白质工程中的应用［J］. 合成生物学， 2025， 6（3）： 636-650 DOI： 10.12211/2096-8280.2025-041.

LI Mingchen， ZHONG Bozitao， YU Yuanxi， JIANG Fan， ZHANG Liang， TAN Yang， YU Huiqun， FAN Guisheng， HONG Liang. DeepSeek model analysis and its applications in AI-assistant protein engineering［J］. Synthetic Biology Journal， 2025， 6（3）： 636-650 DOI： 10.12211/2096-8280.2025-041.

摘要

2025年年初，杭州深度求索人工智能基础技术研究有限公司发布并开源了其自主研发的DeepSeek-R1对话大模型。该模型具备极低的推理成本和出色的思维链推理能力，在多种任务上能够媲美甚至超越闭源的GPT-4o和o1模型，引发了国际社会的高度关注。此外，DeepSeek模型在中文对话上的优异表现以及免费商用的策略，在国内引发了部署和使用的热潮，推动了人工智能技术的普惠与发展。本文围绕DeepSeek模型的架构设计、训练方法与推理机制进行系统性分析，探讨其核心技术在AI蛋白质研究中的迁移潜力与应用前景。DeepSeek模型融合了多项自主创新的前沿技术，包括多头潜在注意力机制、混合专家网络及其负载均衡、低精度训练等，显著降低了Transformer模型的训练和推理成本。尽管DeepSeek模型原生设计用于人类语言的理解与生成，但其优化技术对同样基于Transformer模型的蛋白质预训练语言模型具有重要的参考价值。借助DeepSeek所采用的关键技术，蛋白质语言模型在训练成本、推理成本等方面有望得到显著降低。

Abstract

In early 2025

Hangzhou DeepSeek AI Foundation Technology Research Co.

Ltd. released and open-sourced its independently developed DeepSeek-R1 conversational large language model. This model exhibits extremely low inference costs and outstanding chain-of-thought reasoning capabilities

performing comparably to

and in some tasks surpassing

proprietary models like GPT-4o and o1. This achievement has garnered significant international attention. Furthermore

DeepSeek’s excellent performance in Chinese conversations and its free-for-commercial-use strategy have ignited a wave of deployment and application within China

thereby promoting the widespread adoption and development of AI technology. This work systematically analyzes the architectural design

training methodology

and inference mechanisms of the DeepSeek model

exploring the transfer potential and application prospects of its core technologies in AI-assistant protein research. The DeepSeek model integrates several cutting-edge

independently innovated technologies

including a multi-head latent attention mechanism

mixture-of-experts (MoE) with load balancing

and low-precision training. These innovations have substantially reduced the training and inference costs for Transformer models. Although DeepSeek was originally designed for human language understanding and generation

its optimization techniques hold significant reference value for pre-trained language models with proteins

which are also based on the Transformer architecture. By leveraging the key technologies employed in DeepSeek

protein language models are expected to achieve substantial reductions in training and inference costs.

关键词

Keywords

references

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National Engineering Research Center for Beijing Biochip Technology

Department of Computational Medicine & Bioinformatics， University of Michigan

CAS Key Laboratory of Quantitative Engineering Biology， Shenzhen Institute of Synthetic Biology， Shenzhen Institutes of Advanced Technology， Chinese Academy of Sciences

College of Life Sciences and Medicine， Zhejiang Sci-tech University

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