Suppr超能文献

SHOP:一种基于结构的片段和骨架跳跃方法。

SHOP: a method for structure-based fragment and scaffold hopping.

作者信息

Fontaine Fabien, Cross Simon, Plasencia Guillem, Pastor Manuel, Zamora Ismael

机构信息

Lead Molecular Design, S.L. Av. Cerdanyola 92-94, 08173 Sant Cugat del Vallés, Barcelona, Spain.

出版信息

ChemMedChem. 2009 Mar;4(3):427-39. doi: 10.1002/cmdc.200800355.

Abstract

A new method for fragment and scaffold replacement is presented that generates new families of compounds with biological activity, using GRID molecular interaction fields (MIFs) and the crystal structure of the targets. In contrast to virtual screening strategies, this methodology aims only to replace a fragment of the original molecule, maintaining the other structural elements that are known or suspected to have a critical role in ligand binding. First, we report a validation of the method, recovering up to 95% of the original fragments searched among the top-five proposed solutions, using 164 fragment queries from 11 diverse targets. Second, six key customizable parameters are investigated, concluding that filtering the receptor MIF using the co-crystallized ligand atom type has the greatest impact on the ranking of the proposed solutions. Finally, 11 examples using more realistic scenarios have been performed; diverse chemotypes are returned, including some that are similar to compounds that are known to bind to similar targets.

摘要

本文提出了一种片段和骨架替换的新方法,该方法利用GRID分子相互作用场(MIF)和靶点的晶体结构生成具有生物活性的新化合物家族。与虚拟筛选策略不同,该方法仅旨在替换原始分子的一个片段,保留已知或怀疑在配体结合中起关键作用的其他结构元素。首先,我们报告了该方法的验证情况,使用来自11个不同靶点的164个片段查询,在前五个建议解决方案中最多可找回95%搜索的原始片段。其次,研究了六个关键的可定制参数,得出结论:使用共结晶配体原子类型过滤受体MIF对建议解决方案的排名影响最大。最后,进行了11个使用更现实场景的示例;返回了不同的化学类型,包括一些与已知结合相似靶点的化合物相似的类型。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验