.png){kind=icon}
返回
Interpretable and explainable predictive machine learning models for data-driven protein engineering
.png){kind=icon}
OA
AI
DOI：10.1016/j.biotechadv.2024.108495.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Protein engineering through directed evolution and (semi)rational design has become a powerful approach for optimizing and enhancing proteins with desired properties. The integration of artificial intelligence methods has further accelerated protein engineering process by enabling the development of predictive models based on datadriven strategies. However, the lack of interpretability and transparency in these models limits their trustworthiness and applicability in real-world scenarios. Explainable Artificial Intelligence addresses these challenges by providing insights into the decision-making processes of machine learning models, enhancing their reliability and interpretability. Explainable strategies has been successfully applied in various biotechnology fields, including drug discovery, genomics, and medicine, yet its application in protein engineering remains underexplored. The incorporation of explainable strategies in protein engineering holds significant potential, as it can guide protein design by revealing how predictive models function, benefiting approaches such as machine learning-assisted directed evolution. This perspective work explores the principles and methodologies of explainable artificial intelligence, highlighting its relevance in biotechnology and its potential to enhance protein design. Additionally, three theoretical pipelines integrating predictive models with explainable strategies are proposed, focusing on their advantages, disadvantages, and technical requirements. Finally, the remaining challenges of explainable artificial intelligence in protein engineering and future directions for its development as a support tool for traditional protein engineering methodologies are discussed.
Keyword:
Keywords
Explainable artificial intelligence (XAI)
Explainable machine learning (XML)
Protein engineering
Protein design
Interpretable machine learning (IML)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
12.5
论文数:
3.0K
被引数:
2.7W
机构
暂无机构信息
引用论文
Engineered enzymes for the synthesis of pharmaceuticals and other high-value products.png)
NATURE SYNTHESIS
NATURE SYNTHESISIF20
PREAI
分享收藏Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides通过人工智能发现基于肽的药物: 迈向治疗肽的自主设计OAAI
OAAI分享收藏Explainable Deep Relational Networks for Predicting Compound- Protein Affinities and Contacts可解释的深度关系网络，用于预测化合物-蛋白质的亲和力和联系
OAAI
OAAI分享收藏A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence可解释人工智能的对比和反事实解释生成方法综述IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容