FitScore:基于机器学习的三维虚拟筛选快速评分。

IF 3 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Daniel K. Gehlhaar, Daniel J. Mermelstein
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引用次数: 0

摘要

由于商用化合物数据库日益庞大,而体外筛选成本却没有相应下降,因此提高虚拟筛选富集能力已成为计算化学领域的一个紧迫问题。利用云计算可以对接这些大型数据库。然而,快速对接需要在评分方面做出妥协,这往往会导致富集效果不佳和对接结果中出现大量假阳性。这项工作描述了一种新的评分函数,它由两部分组成:一个是基于知识的组件,用于预测特定原子类型在特定受体环境中的概率;另一个是可调权重矩阵,用于将概率预测转换为适合虚拟筛选富集的无量纲分数。这个分数(FitScore)代表配体与结合位点之间的兼容性,能够在标准化对接测试集中实现高度富集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

FitScore: a fast machine learning-based score for 3D virtual screening enrichment

FitScore: a fast machine learning-based score for 3D virtual screening enrichment

Enhancing virtual screening enrichment has become an urgent problem in computational chemistry, driven by increasingly large databases of commercially available compounds, without a commensurate drop in in vitro screening costs. Docking these large databases is possible with cloud-scale computing. However, rapid docking necessitates compromises in scoring, often leading to poor enrichment and an abundance of false positives in docking results. This work describes a new scoring function composed of two parts – a knowledge-based component that predicts the probability of a particular atom type being in a particular receptor environment, and a tunable weight matrix that converts the probability predictions into a dimensionless score suitable for virtual screening enrichment. This score, the FitScore, represents the compatibility between the ligand and the binding site and is capable of a high degree of enrichment across standardized docking test sets.

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来源期刊
Journal of Computer-Aided Molecular Design
Journal of Computer-Aided Molecular Design 生物-计算机:跨学科应用
CiteScore
8.00
自引率
8.60%
发文量
56
审稿时长
3 months
期刊介绍: The Journal of Computer-Aided Molecular Design provides a form for disseminating information on both the theory and the application of computer-based methods in the analysis and design of molecules. The scope of the journal encompasses papers which report new and original research and applications in the following areas: - theoretical chemistry; - computational chemistry; - computer and molecular graphics; - molecular modeling; - protein engineering; - drug design; - expert systems; - general structure-property relationships; - molecular dynamics; - chemical database development and usage.
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