利用机器学习。

IF 12.9 1区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Nature chemical biology Pub Date : 2024-07-30 DOI:10.1038/s41589-024-01700-7

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引用次数: 0

摘要

根据给定的氨基酸序列计算蛋白质结构的计算方法彻底改变了我们对结构生物学的理解以及对蛋白质结合化合物的预测。本期的几篇文章探讨了蛋白质结构预测的机器学习方法、模型质量的基准测试和评估，以及机器学习算法在药物发现过程中的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Making use of machine learning

Computational methods for calculating a protein structure from a given amino acid sequence have revolutionized both our understanding of structural biology and the prediction of protein-binding compounds. This issue features several pieces that explore machine learning approaches for protein structure prediction, benchmarking and evaluation of model quality, and how machine learning algorithms can be used in the drug discovery process.

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来源期刊

Nature chemical biology 生物-生化与分子生物学

CiteScore

23.90

自引率

1.40%

发文量

238

审稿时长

12 months

期刊介绍： Nature Chemical Biology stands as an esteemed international monthly journal, offering a prominent platform for the chemical biology community to showcase top-tier original research and commentary. Operating at the crossroads of chemistry, biology, and related disciplines, chemical biology utilizes scientific ideas and approaches to comprehend and manipulate biological systems with molecular precision. The journal embraces contributions from the growing community of chemical biologists, encompassing insights from chemists applying principles and tools to biological inquiries and biologists striving to comprehend and control molecular-level biological processes. We prioritize studies unveiling significant conceptual or practical advancements in areas where chemistry and biology intersect, emphasizing basic research, especially those reporting novel chemical or biological tools and offering profound molecular-level insights into underlying biological mechanisms. Nature Chemical Biology also welcomes manuscripts describing applied molecular studies at the chemistry-biology interface due to the broad utility of chemical biology approaches in manipulating or engineering biological systems. Irrespective of scientific focus, we actively seek submissions that creatively blend chemistry and biology, particularly those providing substantial conceptual or methodological breakthroughs with the potential to open innovative research avenues. The journal maintains a robust and impartial review process, emphasizing thorough chemical and biological characterization.