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本文引用的文献

1
Optimizing fragment and scaffold docking by use of molecular interaction fingerprints.利用分子相互作用指纹优化片段和支架对接。
J Chem Inf Model. 2007 Jan-Feb;47(1):195-207. doi: 10.1021/ci600342e.
2
Prediction of aqueous solubility based on large datasets using several QSPR models utilizing topological structure representation.基于大型数据集,利用多种采用拓扑结构表示法的定量构效关系(QSPR)模型预测水溶解度。
Chem Biodivers. 2004 Nov;1(11):1829-41. doi: 10.1002/cbdv.200490137.
3
Bridging chemical and biological space: "target fishing" using 2D and 3D molecular descriptors.连接化学与生物空间:使用二维和三维分子描述符进行“靶点垂钓”
J Med Chem. 2006 Nov 16;49(23):6802-10. doi: 10.1021/jm060902w.
4
In silico-guided target identification of a scaffold-focused library: 1,3,5-triazepan-2,6-diones as novel phospholipase A2 inhibitors.基于计算机辅助的以支架为中心的文库的靶点鉴定:1,3,5-三氮杂环庚烷-2,6-二酮作为新型磷脂酶A2抑制剂
J Med Chem. 2006 Nov 16;49(23):6768-78. doi: 10.1021/jm0606589.
5
Similarity-based virtual screening using 2D fingerprints.使用二维指纹的基于相似性的虚拟筛选。
Drug Discov Today. 2006 Dec;11(23-24):1046-53. doi: 10.1016/j.drudis.2006.10.005. Epub 2006 Oct 20.
6
Ligand-based approach to in silico pharmacology: nuclear receptor profiling.基于配体的计算机辅助药理学方法:核受体分析
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Genetic algorithm-optimized QSPR models for bioavailability, protein binding, and urinary excretion.遗传算法优化的生物利用度、蛋白质结合及尿排泄的定量构效关系模型
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"Bayes affinity fingerprints" improve retrieval rates in virtual screening and define orthogonal bioactivity space: when are multitarget drugs a feasible concept?“贝叶斯亲和力指纹图谱”提高虚拟筛选中的检索率并定义正交生物活性空间:多靶点药物何时成为可行概念?
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用于合理药物设计的化学基因组学方法。

Chemogenomic approaches to rational drug design.

作者信息

Rognan D

机构信息

Bioinformatics of the Drug, Centre National de la Recherche Scientifique UMR 7175-LC1, F-67400 Illkirch, France.

出版信息

Br J Pharmacol. 2007 Sep;152(1):38-52. doi: 10.1038/sj.bjp.0707307. Epub 2007 May 29.

DOI:10.1038/sj.bjp.0707307
PMID:17533416
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1978269/
Abstract

Paradigms in drug design and discovery are changing at a significant pace. Concomitant to the sequencing of over 180 several genomes, the high-throughput miniaturization of chemical synthesis and biological evaluation of a multiple compounds on gene/protein expression and function opens the way to global drug-discovery approaches, no more focused on a single target but on an entire family of related proteins or on a full metabolic pathway. Chemogenomics is this emerging research field aimed at systematically studying the biological effect of a wide array of small molecular-weight ligands on a wide array of macromolecular targets. Since the quantity of existing data (compounds, targets and assays) and of produced information (gene/protein expression levels and binding constants) are too large for manual manipulation, information technologies play a crucial role in planning, analysing and predicting chemogenomic data. The present review will focus on predictive in silico chemogenomic approaches to foster rational drug design and derive information from the simultaneous biological evaluation of multiple compounds on multiple targets. State-of-the-art methods for navigating in either ligand or target space will be presented and concrete drug design applications will be mentioned.

摘要

药物设计与发现的模式正在迅速改变。伴随着180多个基因组的测序,化学合成的高通量微型化以及对多种化合物在基因/蛋白质表达和功能方面的生物学评估,为全球药物发现方法开辟了道路,不再聚焦于单个靶点,而是关注整个相关蛋白质家族或完整的代谢途径。化学基因组学就是这样一个新兴的研究领域,旨在系统地研究一系列小分子配体对一系列大分子靶点的生物学效应。由于现有数据(化合物、靶点和检测方法)的数量以及所产生信息(基因/蛋白质表达水平和结合常数)的数量过于庞大,无法进行人工处理,信息技术在化学基因组数据的规划、分析和预测中发挥着至关重要的作用。本综述将聚焦于基于计算机的预测性化学基因组学方法,以促进合理药物设计,并从对多种化合物在多个靶点上的同步生物学评估中获取信息。将介绍在配体或靶点空间中导航的最新方法,并提及具体的药物设计应用。