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通过结合计算从头设计和人类化学家的专业知识来设计活性模板分子。

Designing active template molecules by combining computational de novo design and human chemist's expertise.

作者信息

Lameijer Eric-Wubbo, Tromp Reynier A, Spanjersberg Ronald F, Brussee Johannes, Ijzerman Adriaan P

机构信息

Leiden/Amsterdam Center for Drug Research, Division of Medicinal Chemistry, Leiden University, Post Office Box 9502, 2300RA Leiden, The Netherlands.

出版信息

J Med Chem. 2007 Apr 19;50(8):1925-32. doi: 10.1021/jm061356+. Epub 2007 Mar 17.

Abstract

We used a new software tool for de novo design, the "Molecule Evoluator", to generate a number of small molecules. Explicit constraints were a relatively low molecular weight and otherwise limited functionality, for example, low numbers of hydrogen bond donors and acceptors, one or two aromatic rings, and a small number of rotatable bonds. In this way, we obtained a collection of scaffold- or templatelike molecules rather than fully "decorated" ones. We asked medicinal chemists to evaluate the suggested molecules for ease of synthesis and overall appeal, allowing them to make structural changes to the molecules for these reasons. On the basis of their recommendations, we synthesized eight molecules with an unprecedented (not patented) yet simple structure, which were subsequently tested in a screen of 83 drug targets, mostly G protein-coupled receptors. Four compounds showed affinity for biogenic amine targets (receptor, ion channel, and transport protein), reflecting the training of the medicinal chemists involved. Apparently the generation of leadlike solutions helped the medicinal chemists to select good starting points for future lead optimization, away from existing compound libraries.

摘要

我们使用了一种用于从头设计的新软件工具“分子进化器”来生成许多小分子。明确的限制条件是相对较低的分子量以及其他有限的官能团,例如,氢键供体和受体数量少、一两个芳香环以及少量可旋转键。通过这种方式,我们获得了一组类似支架或模板的分子,而不是完全“修饰”的分子。我们请药物化学家评估所建议分子的合成难易程度和总体吸引力,允许他们出于这些原因对分子进行结构改变。根据他们的建议,我们合成了八个具有前所未有的(未获专利)且简单结构的分子,随后在83个药物靶点(主要是G蛋白偶联受体)的筛选中对其进行了测试。四种化合物对生物胺靶点(受体、离子通道和转运蛋白)表现出亲和力,这反映了参与其中的药物化学家的经验。显然,生成类先导化合物的解决方案有助于药物化学家从现有的化合物库中挑选出未来先导化合物优化的良好起点。

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