Drug-like annotation and duplicate analysis of a 23-supplier chemical database totalling 2.7 million compounds

被引：104

作者：

Baurin, N ^{[1
]}

Baker, R ^{[1
]}

Richardson, C ^{[1
]}

Chen, I ^{[1
]}

Foloppe, N ^{[1
]}

Potter, A ^{[1
]}

Jordan, A ^{[1
]}

Roughley, S ^{[1
]}

Parratt, M ^{[1
]}

Greaney, P ^{[1
]}

Morley, D ^{[1
]}

Hubbard, RE ^{[1
]}

机构：

[1] Vernalis Cambridge Ltd, Cambridge CB1 6GB, England

来源：

JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES | 2004年 / 44卷 / 02期

关键词：

D O I：

10.1021/ci034260m

中图分类号：

O6 [化学];

学科分类号：

0703 [化学];

摘要：

We have implemented five drug-like filters, based on ID and 2D molecular descriptors, and applied them to characterize the drug-like properties of commercially available chemical compounds. In addition to previously published filters (Lipinski and Veber), we implemented a filter for medicinal chemistry tractability based on lists of chemical features drawn up by a panel of medicinal chemists. A filter based on the modeling of aqueous solubility (>1 muM) was derived in-house, as well as another based on the modeling of Caco-2 passive membrane permeability (>10 nm/s). A library of 2.7 million compounds was collated from the 23 compound suppliers and analyzed with these filters, highlighting a tendency toward highly lipophilic compounds. The library contains 1.6M unique structures, of which 37% (607 223) passed all five drug-like filters. None of the 23 suppliers provides all the members of the drug-like subset, emphasizing the benefit of considering compounds from various compound suppliers as a source of diversity for drug discovery.

引用

页码：643 / 651

页数：9

共 37 条

[1]

Can we learn to distinguish between "drug-like" and "nondrug-like" molecules? [J].