GB-score:基于距离加权原子间接触特征的最小设计机器学习评分功能。

IF 2.8 4区 医学 Q3 CHEMISTRY, MEDICINAL
Milad Rayka, Rohoullah Firouzi
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引用次数: 3

摘要

近年来,由于计算机硬件和数据集可用性的进步,数据驱动的方法(如机器学习)已成为加速药物发现程序的药物设计框架的重要组成部分之一。基于机器和深度学习构建一个新的评分函数,该函数可以预测在对接过程中生成的蛋白质-配体姿态或晶体复合物的结合分数,已成为计算机辅助药物设计的一个活跃研究领域。GB-Score是一种基于机器学习的评分功能,它利用距离加权原子间接触特征、PDBbind-v2019通用集和梯度增强树算法来预测绑定亲和力。距离加权原子间接触表征方法利用不同配体与蛋白质原子类型之间的距离对蛋白质-配体复合物进行数值表征。GB-Score在评分能力指标CASF-2016基准测试中达到Pearson相关性0.862和RMSE 1.190。GB-Score的代码可以在https://github.com/miladrayka/GB_Score网站上免费获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

GB-score: Minimally designed machine learning scoring function based on distance-weighted interatomic contact features.

GB-score: Minimally designed machine learning scoring function based on distance-weighted interatomic contact features.

In recent years, thanks to advances in computer hardware and dataset availability, data-driven approaches (like machine learning) have become one of the essential parts of the drug design framework to accelerate drug discovery procedures. Constructing a new scoring function, a function that can predict the binding score for a generated protein-ligand pose during docking procedure or a crystal complex, based on machine and deep learning has become an active research area in computer-aided drug design. GB-Score is a state-of-the-art machine learning-based scoring function that utilizes distance-weighted interatomic contact features, PDBbind-v2019 general set, and Gradient Boosting Trees algorithm to the binding affinity prediction. The distance-weighted interatomic contact featurization method used the distance between different ligand and protein atom types for numerical representation of the protein-ligand complex. GB-Score attains Pearson's correlation 0.862 and RMSE 1.190 on the CASF-2016 benchmark test in the scoring power metric. GB-Score's codes are freely available on the web at https://github.com/miladrayka/GB_Score.

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来源期刊
Molecular Informatics
Molecular Informatics CHEMISTRY, MEDICINAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.30
自引率
2.80%
发文量
70
审稿时长
3 months
期刊介绍: Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics succeeded QSAR & Combinatorial Science in 2010. Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation. The journal''s scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules. As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.
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