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虚拟筛选中分子指纹相似性搜索概述

An overview of molecular fingerprint similarity search in virtual screening.

作者信息

Muegge Ingo, Mukherjee Prasenjit

机构信息

a Boehringer Ingelheim Pharmaceuticals , Department of Small Molecule Discovery Research , Ridgefield , CT , USA.

出版信息

Expert Opin Drug Discov. 2016;11(2):137-48. doi: 10.1517/17460441.2016.1117070. Epub 2015 Dec 4.

Abstract

INTRODUCTION

A central premise of medicinal chemistry is that structurally similar molecules exhibit similar biological activities. Molecular fingerprints encode properties of small molecules and assess their similarities computationally through bit string comparisons. Based on the similarity to a biologically active template, molecular fingerprint methods allow for identifying additional compounds with a higher chance of displaying similar biological activities against the same target - a process commonly referred to as virtual screening (VS).

AREAS COVERED

This article focuses on fingerprint similarity searches in the context of compound selection for enhancing hit sets, comparing compound decks, and VS. In addition, the authors discuss the application of fingerprints in predictive modeling.

EXPERT OPINION

Fingerprint similarity search methods are especially useful in VS if only a few unrelated ligands are known for a given target and therefore more complex and information rich methods such as pharmacophore searches or structure-based design are not applicable. In addition, fingerprint methods are used in characterizing properties of compound collections such as chemical diversity, density in chemical space, and content of biologically active molecules (biodiversity). Such assessments are important for deciding what compounds to experimentally screen, to purchase, or to assemble in a virtual compound deck for in silico screening or de novo design.

摘要

引言

药物化学的一个核心前提是结构相似的分子具有相似的生物活性。分子指纹编码小分子的特性,并通过位串比较在计算上评估它们的相似性。基于与生物活性模板的相似性,分子指纹方法能够识别出更有可能对同一靶点表现出相似生物活性的其他化合物——这一过程通常称为虚拟筛选(VS)。

涵盖领域

本文重点关注在化合物选择背景下的指纹相似性搜索,以增强命中集、比较化合物库和进行虚拟筛选。此外,作者还讨论了指纹在预测建模中的应用。

专家观点

如果对于给定靶点仅知道少数不相关的配体,因此诸如药效团搜索或基于结构的设计等更复杂且信息丰富的方法不适用,那么指纹相似性搜索方法在虚拟筛选中特别有用。此外,指纹方法用于表征化合物集合的特性,如化学多样性、化学空间密度和生物活性分子含量(生物多样性)。此类评估对于决定实验筛选、购买哪些化合物或在虚拟化合物库中组装哪些化合物以进行计算机筛选或从头设计很重要。

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